Machine learning using clinical and simulated data

ABSTRACT

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.62/663,049, filed on Apr. 26, 2018, entitled “MACHINE LEARNING USINGSIMULATED CARDIOGRAMS,” which is hereby incorporated by reference in itsentirety.

This application is related to the following applications filedconcurrently: U.S. application Ser. No. ______ (Attorney Docket No.129292-8003.US00), entitled “GENERATING SIMULATED ANATOMIES OF ANELECTROMAGNETIC SOURCE”; U.S. application Ser. No. ______ (AttorneyDocket No. 129292-8004.US00), entitled “GENERATING A MODEL LIBRARY OFMODELS OF AN ELECTROMAGNETIC SOURCE”; U.S. application Ser. No. ______(Attorney Docket No. 129292-8005.US00), entitled “USER INTERFACE FORPRESENTING SIMULATED ANATOMIES OF AN ELECTROMAGNETIC SOURCE”; U.S.application Ser. No. ______ (Attorney Docket No. 129292-8006.US00),entitled “CONVERTING A POLYHEDRAL MESH REPRESENTING AN ELECTROMAGNETICSOURCE”; U.S. application Ser. No. ______ (Attorney Docket No.129292-8007.US00), entitled “GENERATING APPROXIMATIONS OF CARDIOGRAMSFROM DIFFERENT SOURCE CONFIGURATIONS”; U.S. application Ser. No. ______(Attorney Docket No. 129292-8008.US00), entitled “BOOTSTRAPPING ASIMULATION-BASED ELECTROMAGNETIC OUTPUT OF A DIFFERENT ANATOMY”; U.S.application Ser. No. ______ (Attorney Docket No. 129292-8009.US00),entitled “IDENTIFYING AN ATTRIBUTE OF AN ELECTROMAGNETIC SOURCECONFIGURATION BY MATCHING SIMULATED AND PATIENT DATA”; U.S. applicationSer. No. ______ (Attorney Docket No. 129292-8011.US00), entitled“DISPLAY OF AN ELECTROMAGNETIC SOURCEBASED ON A PATIENT-SPECIFIC MODEL”;and U.S. application Ser. No. ______ (Attorney Docket No.129292-8012.US00), entitled “DISPLAY OF AN ELECTRICAL FORCE GENERATED BYAN ELECTRICAL SOURCE WITHIN A BODY,” each is hereby incorporated byreference in its entirety.

BACKGROUND

Many heart disorders can cause symptoms, morbidity (e.g., syncope orstroke), and mortality. Common heart disorders caused by arrhythmiasinclude inappropriate sinus tachycardia (“IST”), ectopic atrial rhythm,junctional rhythm, ventricular escape rhythm, atrial fibrillation(“AF”), ventricular fibrillation (“VF”), focal atrial tachycardia(“focal AT”), atrial microreentry, ventricular tachycardia (“VT”),atrial flutter (“AFL”), premature ventricular complexes (“PVCs”),premature atrial complexes (“PACs”), atrioventricular nodal reentranttachycardia (“AVNRT”), atrioventricular reentrant tachycardia (“AVRT”),permanent junctional reciprocating tachycardia (“PJRT”), and junctionaltachycardia (“JT”). The sources of arrhythmias may include electricalrotors (e.g., ventricular fibrillation), recurring electrical focalsources (e.g., atrial tachycardia), anatomically based reentry (e.g.,ventricular tachycardia), and so on. These sources are important driversof sustained or clinically significant episodes. Arrhythmias can betreated with ablation using different technologies, includingradiofrequency energy ablation, cryoablation, ultrasound ablation, laserablation, external radiation sources, directed gene therapy, and so onby targeting the source of the heart disorder. Since the sources ofheart disorders and the locations of the source vary from patient topatient, even for common heart disorders, targeted therapies require thesource of the arrhythmia to be identified.

Unfortunately, current methods for reliably identifying the sourcelocations of the source of a heart disorder can be complex, cumbersome,and expensive. For example, one method uses an electrophysiologycatheter having a multi-electrode basket catheter that is inserted intothe heart (e.g., left ventricle) intravascularly to collect from withinthe heart measurements of the electrical activity of the heart, such asduring an induced episode of VF. The measurements can then be analyzedto help identify a possible source location. Presently,electrophysiology catheters are expensive (and generally limited to asingle use) and may lead to serious complications, including cardiacperforation and tamponade. Another method uses an exterior body surfacevest with electrodes to collect measurements from the patient's bodysurface, which can be analyzed to help identify an arrhythmia sourcelocation. Such body surface vests are expensive, are complex anddifficult to manufacture, and may interfere with the placement ofdefibrillator pads needed after inducing VF to collect measurementsduring the arrhythmia. In addition, the vest analysis requires acomputed tomography (“CT”) scan and is unable to sense theinterventricular and interatrial septa where approximately 20% ofarrhythmia sources may occur.

BRIEF DESCRIPTION OF THE DRAWINGS

This application contains at least one drawing executed in color. Copiesof this application with color drawing(s) will be provided by the Officeupon request and payment of the necessary fees.

FIG. 1 is a block diagram that illustrates the overall processing of theMLMO system in some embodiments.

FIG. 2 is a flow diagram that illustrates the overall processing ofgenerating a classifier by the MLMO system in some embodiments.

FIG. 3 is a block diagram that illustrates training and classifyingusing a convolutional neural network in some embodiments.

FIG. 4 is a flow diagram that illustrates processing of a generateclassifier component of the MLMO system in some embodiments.

FIG. 5 is a flow diagram that illustrates the processing of a generatesimulated VCGs component of the MLMO system in some embodiments.

FIG. 6 is a flow diagram that illustrates the processing of a generatetraining data component for cycles of the MLMO system in someembodiments.

FIG. 7 is a flow diagram that illustrates the processing of an identifycycles component of the MLMO system in some embodiments.

FIG. 8 is a block diagram that illustrates the processing of a normalizecycle component of the MLMO system in some embodiments.

FIG. 9 is a flow diagram that illustrates processing of a generatetraining data for a sequence of similar cycles component of the MLMOsystem in some embodiments.

FIG. 10 is a flow diagram that illustrates the processing of a classifycomponent of the MLMO system in some embodiments.

FIG. 11 is a block diagram illustrating components of the MLG system insome embodiments.

FIG. 12 is a block diagram that illustrates the generating of asimulated anatomy from seed anatomies.

FIG. 13 is a display page that illustrates a user experience for viewingsimulated anatomies in some embodiments.

FIG. 14 is a flow diagram that illustrates the processing of a generatemodel library component of the MLG system in some embodiments.

FIG. 15 is a flow diagram that illustrates the processing of a generatesimulated anatomies component of the MLG system in some embodiments.

FIG. 16 is a flow diagram that illustrates the processing of a generatesimulated anatomy component of the MLG system in some embodiments.

FIG. 17 is a flow diagram that illustrates the processing of a generatesource configurations component of the MLG system in some embodiments.

FIG. 18 is a flow diagram that illustrates the processing of a generatemodel component of the MLG system in some embodiments.

FIG. 19 is a flow diagram that illustrates the processing of a displaysimulated anatomy component of the MLG system in some embodiments.

FIG. 20 is a block diagram that illustrates the process of bootstrappinga simulation based on an EM mesh of a prior simulation with similaranatomical parameters.

FIG. 21 is a flow diagram that illustrates the processing of a componentto generate a voltage solution for a representative arrhythmia model ofa group of the MLG system in some embodiments.

FIG. 22 is a flow diagram that illustrates the processing of a generatevoltage solution component for a group of arrhythmia models based on arepresentative voltage solution of the MLG system in some embodiments.

FIG. 23 is a block diagram that illustrates the process of approximatinga cardiogram based on a voltage solution for an arrhythmia model withdifferent anatomical parameters.

FIG. 24 is a flow diagram that illustrates the processing of anapproximate VCG component of the MLG system in some embodiments.

FIG. 25 is a block diagram that illustrates the process of converting anarrhythmia model based on a first polyhedron to an arrhythmia modelbased on a second polyhedron in some embodiments.

FIG. 26 is a flow diagram that illustrates the processing of a convertpolyhedral model component of the MLG system in some embodiments.

FIG. 27 is a flow diagram that illustrates the processing of an identifyattributes component of the PM system in some embodiments.

FIG. 28 is a flow diagram that illustrates the processing of an identifymatching VCGs component of the PM system in some embodiments.

FIG. 29 is a flow diagram that illustrates the processing of an identifyclasses based on clustering component of the PM system in someembodiments.

FIG. 30 is a flow diagram that illustrates the processing of a generatecluster classifiers component of the PM system in some embodiments.

FIG. 31 is a block diagram that illustrates the overall processing of apatient classifier system of a MLCD system in some embodiments.

FIG. 32 is a flow diagram that illustrates the processing of a generatepatient classifier component of the patient classifier system in someembodiments.

FIG. 33 is a flow diagram that illustrates the processing of a generatecluster patient classifier of the patient classifier system in someembodiments.

FIG. 34 is a block diagram that illustrates components of apatient-specific model classifier system of a MLCD system in someembodiments.

FIG. 35 is a flow diagram that illustrates processing of a generatepatient-specific model classifier component of the PSMC system in someembodiments.

FIG. 36 is a flow diagram that illustrates the processing of an identifysimilar models component of the PSMC system in some embodiments.

FIG. 37 is a block diagram that illustrates the overall processing of apatent-specific model display system in some embodiments.

FIG. 38 is a flow diagram that illustrates the processing of a generatepatient heart display component of the PSMD system in some embodiments.

FIG. 39 is a flow diagram that illustrates the processing of a calculatedisplay values component of the PSMD system in some embodiments.

FIG. 40 illustrates various surface representations ofvectorcardiograms.

FIG. 41 is a flow diagram that illustrates the processing of a visualizeVCG component of the EFD system in some embodiments.

FIG. 42 is a flow diagram that illustrates the processing of a displayVCG surface representation component of the EFD system in someembodiments.

DETAILED DESCRIPTION

Methods and systems are provided for generating data representingelectromagnetic states (e.g., normal sinus rhythm versus ventricularfibrillation) of an electromagnetic source (e.g., a heart) within a bodyfor various purposes such as medical, scientific, research, andengineering purposes. The systems generate the data based on sourceconfigurations (e.g., dimensions of, and scar or fibrosis orpro-arrhythmic substrate location within, a heart) of theelectromagnetic source and a computational model of the electromagneticoutput of the electromagnetic source. The systems may dynamicallygenerate the source configurations to provide representative sourceconfigurations that may be found in a population. For each sourceconfiguration of the electromagnetic source, the systems run asimulation of the functioning of the electromagnetic source to generatemodeled electromagnetic output for that source configuration. Thesystems generate derived electromagnetic data (e.g., a vectorcardiogram)for each source configuration from the modeled electromagnetic output ofthat source configuration. The system may then use the modeledelectromagnetic output for various purposes such as identifyinglocations of disorders within the electromagnetic source of a patient'sbody, guiding a procedure to repair (e.g., directed gene therapy) ormodify (e.g., ablate) the electromagnetic source, predicting results ofa procedure on the electromagnetic source, analyzing genetic defects,and so on. The systems may include a machine learning based on modeledoutput system and a model library generation system, which are describedbelow.

Machine Learning Based on Modeled Output System

A method and a system are provided for generating a classifier forclassifying electromagnetic data derived from an electromagnetic sourcewithin a body. A body may be, for example, a human body, and theelectromagnetic source may be a heart, a brain, a liver, a lung, akidney, or another part of the body that generates an electromagneticfield that can be measured from outside or inside the body. Theelectromagnetic fields can be measured by various measuring devices(e.g., electrocardiograph and electroencephalograph) using, for example,one or more (e.g., 12) leads connected to electrodes attached to oradjacent to (e.g., via a smart watch device) a patient's body, a bodysurface vest worn by a patient, an intra-electromagnetic source device(e.g., a basket catheter), a cap worn by a patient, and so on. Themeasurements can be represented via a cardiogram such as anelectrocardiogram (“ECG”) and a vectorcardiogram (“VCG”), anelectroencephalogram (“EEG”), and so on. In some embodiments, a machinelearning based on modeled output (“MLMO”) system is provided to generatea classifier by modeling electromagnetic output of the electromagneticsource for a variety of source configurations and using machine learningto train a classifier using derived electromagnetic data that is derivedfrom the modeled electromagnetic output as training data. The MLMOsystem is described below primarily to generate a classifier forelectromagnetic data of the heart.

In some embodiments, the MLMO system employs a computational model ofthe electromagnetic source to generate training data for training theclassifier. A computational model models electromagnetic output of theelectromagnetic source over time based on a source configuration of theelectromagnetic source. The electromagnetic output may representelectrical potential, a current, a magnetic field, and so on. When theelectromagnetic (“EM”) source is a heart, the source configuration mayinclude any subset of the group consisting of information on geometryand muscle fibers of the heart, torso anatomy, normal and abnormalcardiac anatomy, normal and abnormal cardiac tissue, scar, fibrosis,inflammation, edema, accessory pathways, congenital heart disease,malignancy, sites of prior ablation, sites of prior surgery, sites ofexternal radiation therapy, pacing leads, implantablecardioverter-defibrillator leads, cardiac resynchronization therapyleads, pacemaker pulse generator location, implantablecardioverter-defibrillator pulse generator location, subcutaneousdefibrillator lead location, subcutaneous defibrillator pulse generatorlocation, leadless pacemaker location, other implanted hardware (e.g.,right or left ventricular assist devices), external defibrillationelectrodes, surface ECG leads, surface mapping leads, a mapping vest,other normal and pathophysiologic feature distributions, and so on, andthe EM output is a collection of the electric potentials at variousheart locations over time. To generate the EM output, a simulation maybe performed for simulation steps of a step size (e.g., 1 ms) togenerate an EM mesh for that step. The EM mesh may be a finite-elementmesh that stores the value of the electric potential at each heartlocation for that step. For example, the left ventricle may be definedas having approximately 70,000 heart locations with the EM mesh storingan electromagnetic value for each heart location. If so, a three-secondsimulation with a step size of 1 ms would generate 3,000 EM meshes thateach include 70,000 values. The collection of the EM meshes is the EMoutput for the simulation. A computational model is described in C. T.Villongco, D. E. Krummen, P. Stark, J. H. Omens, & A. D. McCulloch,“Patient-specific modeling of ventricular activation pattern usingsurface ECG-derived vectorcardiogram in bundle branch block,” Progressin Biophysics and Molecular Biology, Volume 115, Issues 2-3, August2014, Pages 305-313, which is hereby incorporated by reference. In someembodiments, the MLMO system may generate values for points between thevertices as the mesh, rather than just at the vertices. For example, theMLMO system may calculated the values for such points using a Gaussianquadrature technique.

In some embodiments, the MLMO system generates the training data byrunning many simulations, each based on a different sourceconfiguration, which is a set of different values for the configurationparameters of the computational model. For example, the configurationparameters for the heart may be cardiac geometry, rotor location, focalsource location, ventricular orientation in the chest, ventricularmyofiber orientation, cardiomyocyte intracellular potentialelectrogenesis and propagation, and so on. Each configuration parametermay have a set or range of possible values. For example, the rotorlocation may be 78 possible parameter sets corresponding to differentlocations within a ventricle. Since the MLMO system may run a simulationfor each combination of possible values, the number of simulations maybe in the millions.

In some embodiments, the MLMO system uses EM outputs of the simulationsto train the classifier for the generation of a classification based onEM data collected from a patient. The MLMO system may generate derivedEM data for each EM output of a simulation. The derived EM datacorrespond to EM data generated based on measurements that would becollected by an EM measuring devices that use, for example, 12 leads togenerate an ECG or a VCG, a body surface vest, an intra-electromagneticsource device, and so on. The ECG and VCG are equivalent sourcerepresentations of the EM output. The MLMO then generates a label (orlabels) for each derived EM data to specify its correspondingclassification. For example, the MLMO system may generate a label thatis the value of a configuration parameter (e.g., rotor location) usedwhen generating the EM output from which the EM data was derived. Thecollection of the derived EM data, which correspond to feature vectors,and their labels compose the training data for training the classifier.The MLMO system then trains the classifier. The classifier may be any ofa variety or combination of classifiers including neural networks suchas fully-connected, convolutional, recurrent, autoencoder, or restrictedBoltzmann machine, a support vector machine, a Bayesian classifier, andso on. When the classifier is a deep neural network, the trainingresults in a set of weights for the activation functions of the deepneural network. The classifier selected may be based on the type ofdisorder to be identified. For example, certain types of neural networksmay be able to effectively train based on focal sources, but not rotorsources.

In some embodiments, the MLMO system may augment the training data withadditional features from the configuration parameters of the sourceconfiguration used to generate the training data. For example, the MLMOsystem may generate additional features to represent the geometry of theheart, the orientation of the heart, scar or fibrosis or pro-arrhythmicsubstrate location or locations, ablation location, ablation shape, andso on. The MLMO system may input these additional features into thefully connected layer along with the output generated by the layerbefore the fully connected layer of a convolutional neural network(“CNN”), which is described below. The output of the layer before thefully connected layer (e.g., pooling layer) may be “flattened” into aone-dimensional array, and the MLMO system may add the additionalfeatures as further elements of the one-dimensional array. The output ofthe fully connected layer may provide a probability for each label usedin the training data. The probabilities will thus be based on thecombination of the derived EM data and the additional features. Theclassifier will be able to output different probabilities even when thederived EM data is the same or similar to reflect, for example, that thesame or similar EM data may be generated for patients with differentheart geometries and different scar or fibrosis or pro-arrhythmicsubstrate locations. The MLMO system may alternatively employ anadditional classifier that (1) inputs the probabilities generated by theCNN based only on the derived EM data and (2) inputs the additionalfeatures and then outputs a final probability for each classificationthat factors in the additional features. The additional classifier maybe, for example, a support vector machine. The CNN and the additionalclassifier may be trained in parallel.

In some embodiments, the MLMO system normalizes the VCGs of each cycleof the training data in both the voltage and time axes. A cycle may bedefined as a time interval (e.g., start time to end time) defining asingle unit or beat of periodic electrical activity during normal orabnormal rhythms. Cycles facilitate beat-by-beat analysis of sourceconfiguration evolution over time and enable subsequent voltage and timenormalization over each cycle. Normalization preserves salient featuresof voltage-time dynamics and improves generalizability of the trainingdata to variations in source configuration parameters (e.g., torsoconductivities, lead placement and resistance, myocardial conductionvelocity, action potential dynamics, overall heart size, etc.)anticipated in real patients. The MLMO system may normalize the voltagesto a range between −1 and 1 and the time to a fixed range of 0 to 1 inincrements of milliseconds or percentages. To normalize the voltages fora cycle, the MLMO system may identify the maximum magnitude of thevectors across the axes. The MLMO system divides each voltage by themaximum magnitude. To normalize the time axis, the MLMO system performsan interpolation from the number of points in the VCG, which may be moreor less than 1000, to the 1000 points of the normalized cycle.

In some embodiments, after the classifier is trained, the MLMO system isready to generate classifications based on EM and other routinelyavailable clinical data collected from patients. For example, an ECG maybe collected from a patient, and a VCG may be generated from the ECG.The VCG is input to the classifier to generate a classificationindicating, for example, a rotor location for the patient. As a result,even though the geometry of the patient's heart is not known or nosimulation was based on the same geometry as the patient's heart, theMLMO system can be used to generate a classification. If other patientmeasurements such as cardiac dimensions and orientation, scar orfibrosis or pro-arrhythmic substrate configuration, etc. are available,they may be included as input with the EM data to improve accuracy. Thisallows the classifier to effectively learn complex hidden features invarious clinical data that are not directly represented by the trainingdata.

In some embodiments, the MLMO system may classify source stability(i.e., the beat-to-beat consistency of a dominant arrhythmia sourcelocalized to a particular region in the heart) by generating trainingdata that is based on sequences of consecutive cycles that have similarEM features. A technique for determining the stability of arrhythmiasources is described in Krummen, D., et al., Rotor Stability SeparatesSustained Ventricular Fibrillation from Self-Terminating Episodes inHumans, Journal of American College of Cardiology, Vol. 63, No. 23,2014, which is hereby incorporated by reference. This referencedemonstrates the efficacy of targeted ablation at stable source sitesfor preventing recurring arrhythmic episodes. For example, given a VCG,the MLMO system may identify the cycles and then identify sequences oftwo consecutive cycles, three consecutive cycles, four consecutivecycles, and so on in which all the VCG cycles in the sequence are ofsimilar morphology to each other. Each identified sequence may belabeled based on the value of a parameter of the source configurationused to generate the VCG. The MLMO system may then train a separateclassifier for each sequence length (e.g., 2, 3, 4, and so on) using thetraining data for the sequences of that sequence length. For example,the MLMO system may train a classifier for sequences of two cycles and aseparate classifier for sequences of three cycles. To generate aclassification for a patient, the MLMO system may identify sequences ofsimilar cycles of varying sequence lengths in the VCG of the patient andinput those sequences into the classifier for the appropriate sequencelength. The MLMO system may then combine the classifications from allthe classifiers to arrive at a final classification or may simply outputall the classifications.

Although a classifier could be trained using actual patient ECGs or VCGsand corresponding intracardiac basket catheter measurements of sourcelocation, the cost of collecting, preparing, and labeling a sufficientnumber of data would be prohibitive. Moreover, training data based onactual patients would likely be too sparse and noisy to be effective attraining a classifier for a large population. In some embodiments, theMLMO system could be trained using a combination of actual patient VCGsand VCGs derived from simulations.

FIG. 1 is a block diagram that illustrates the overall processing of theMLMO system in some embodiments. The MLMO system includes classifiergeneration components 110 and classification components 120. Thecomputational model for a heart is a heart model that may include dataand code stored in a heart model data store 111. A generate simulationscomponent 112 inputs the heart model and the parameter sets for thesimulations. The parameter sets, also referred to as sourceconfigurations, may include a parameter set for each combination ofpossible values of the parameters or may specify how to generate (e.g.,via a computer code) the parameter sets. For example, the computer codefor the rotor location parameter may include a list of possible rotorlocations and for the ventricle orientation parameter may dynamicallygenerate the values from a base orientation axis along with code forgenerating possible tilt angles from that base orientation such as anx-axis and a y-axis increment. The output of the generate simulationscomponent is stored in a voltage solutions data store 113 where avoltage solution is an EM output. A voltage solution is an example of anEM mesh. A generate VCGs component 114 generates a VCG from the voltagesolutions and stores the VCG in a VCG data store 115. The generate VCGscomponent may generate an ECG from the voltage solutions and thengenerate a VCG from the ECG. The generation of a VCG from an ECG isdescribed in J. A. Kors, G. Van Herpen, A. C. Sittig, & J. H. VanBemmel, “Reconstruction of the Frank vectorcardiogram from standardelectrocardiographic leads: diagnostic comparison of different methods,”European Heart Journal, Volume 11, Issue 12, 1 Dec. 1990, Pages1083-1092, which is hereby incorporated by reference. A generatetraining data component 116 inputs the VCGs and labels each VCG with alabel or labels that may be derived from the parameter sets and storesthe training data in a training data store 117. A label may be, forexample, the value of a parameter of the parameter set used to generatethe EM output from which the VCG is derived. A train classifiercomponent 118 inputs the training data, trains a classifier, and storesthe weights (e.g., of activation functions of a convolutional neuralnetwork) in a classifier weights data store 119. To generate aclassification, a collect ECG component 121 inputs an ECG collected froma patient. A generate VCG component 122 generates a VCG from the ECG. Aclassify component 123 inputs the VCG and generates a classificationusing the classifier weights of the trained classifier.

The computing systems (e.g., network nodes or collections of networknodes) on which the MLMO system and the other described systems may beimplemented may include a central processing unit, input devices, outputdevices (e.g., display devices and speakers), storage devices (e.g.,memory and disk drives), network interfaces, graphics processing units,cellular radio link interfaces, global positioning system devices, andso on. The input devices may include keyboards, pointing devices, touchscreens, gesture recognition devices (e.g., for air gestures), head andeye tracking devices, microphones for voice recognition, and so on. Thecomputing systems may include high-performance computing systems,cloud-based servers, desktop computers, laptops, tablets, e-readers,personal digital assistants, smartphones, gaming devices, servers, andso on. For example, the simulations and training may be performed usinga high-performance computing system, and the classifications may beperformed by a tablet. The computing systems may accesscomputer-readable media that include computer-readable storage media anddata transmission media. The computer-readable storage media aretangible storage means that do not include a transitory, propagatingsignal. Examples of computer-readable storage media include memory suchas primary memory, cache memory, and secondary memory (e.g., DVD) andother storage. The computer-readable storage media may have recorded onthem or may be encoded with computer-executable instructions or logicthat implements the MLMO system and the other described systems. Thedata transmission media are used for transmitting data via transitory,propagating signals or carrier waves (e.g., electromagnetism) via awired or wireless connection. The computing systems may include a securecryptoprocessor as part of a central processing unit for generating andsecurely storing keys and for encrypting and decrypting data using thekeys.

The MLMO system and the other described systems may be described in thegeneral context of computer-executable instructions, such as programmodules and components, executed by one or more computers, processors,or other devices. Generally, program modules or components includeroutines, programs, objects, data structures, and so on that performtasks or implement data types of the MLMO system and the other describedsystems. Typically, the functionality of the program modules may becombined or distributed as desired in various examples. Aspects of theMLMO system and the other described systems may be implemented inhardware using, for example, an application-specific integrated circuit(“ASIC”) or field programmable gate array (“FPGA”).

FIG. 2 is a flow diagram that illustrates the overall processing ofgenerating a classifier by the MLMO system in some embodiments. Agenerate classifier component 200 is executed to generate a classifier.In block 201, the component accesses the computational model to be usedto run the simulations. In block 202, the component selects the nextsource configuration (i.e., parameter set) to be used in a simulation.In decision block 203, if all the source configurations have alreadybeen selected, then the component continues at block 205, else thecomponent continues at block 204. In block 204, the component runs thesimulation using the selected source configuration to generate an EMoutput for the simulation and then loops to block 202 to select the nextsource configuration. In block 205, the component selects the next EMoutput that was generated by a simulation. In decision block 206, if allthe EM outputs have already been selected, then the component continuesat block 210, else the component continues at block 207. In block 207,the component derives the EM data from the EM output. For example, theEM output may be a collection of EM meshes, and the EM data may be anECG or a VCG derived from the electromagnetic values of the EM mesh. Insome embodiments, the component may in addition identify cycles(periodic intervals of arrhythmic activity) within the ECG or VCG. Acycle may be delimited by successive crossings from a negative voltageto a positive voltage (“positive crossings”) or successive crossingsfrom a positive voltage to a negative voltage (“negative crossings”)with respect to a spatial direction or set of directions comprising areference frame or set of reference frames. A reference frame maycoincide with anatomical axes (e.g., left-to-right with x,superior-to-inferior with y, anterior-to-posterior with z), imaging axes(e.g., CT, MR, or x-ray coordinate frames), body-surface lead vectors,principal axes computed by principal component analysis of measured orsimulated EM source configurations and outputs, or user-defineddirections of interest. For example, a three-second VCG may have threecycles, and each cycle may be delimited by the times of the positivecrossings along the x-axis. Alternatively, the cycles may be delimitedby crossings along the y-axis or z-axis. In addition, cycles may bedefined by negative crossings. Thus, in some embodiments, the componentmay generate training data from a single VCG based on various cycledefinitions that are various combinations of positive crossings andnegative crossings with the cycles for all the axes being defined bycrossings on one of the x-axis, y-axis, and z-axis or the cycles foreach defined by crossings on that axis. Moreover, the training data mayinclude cycles identified based on all possible cycle definitions or asubset of the cycle definition. For example, the training data mayinclude, for each axis, a cycle defined by positive crossings of thex-axis, negative crossings of the y-axis, and positive crossings of thataxis itself. Cycles definitions may also be defined by the timing ofelectrical events derived from the values stored in the EM mesh. Forexample, a point or set of points in the mesh may periodically crossvoltage thresholds signifying electrical activation and deactivation.Thus, a cycle may be defined by activation-deactivation, or successiveactivation-activation or deactivation-deactivation intervalscorresponding to a point or set of points within the mesh. The resultingtimings of these intervals can be co-localized to the ECG or VCG forcycles identification. In block 208, the component labels the EM databased on the source configuration (e.g., a source location). When cyclesare identified, the component may label each cycle with the same label.For example, the component may label the identified cycles with the samerotor location. In block 209, the component adds the EM data along withthe label to the training data and then loops to block 205 to select thenext EM output. In block 210, the component trains the classifier usingthe training data and then completes.

FIG. 3 is a block diagram that illustrates training and classifyingusing a convolutional neural network in some embodiments. Theconvolutional neural network may be one-dimensional in the sense that itinputs an image that is a single row of pixels with each pixel having ared, green, and blue (“RGB”) value. The MLMO system sets the values ofthe pixels based on the voltages of a VCG of the training data. Theimage has the same number of pixels as vectors of a VCG of the trainingdata. The MLMO system sets the red, green, and blue values of a pixel ofthe image to the x, y, and z values of the corresponding vector of theVCG. For example, if a cycle of a VCG is 1 second long, and the VCG hasa vector for each millisecond, then the image is 1 by 1000 pixels. Theone-dimensional convolutional neural network (“1D CNN”) trainer 310learns the weights of activation functions for the convolutional neuralnetwork using the training data 301. To generate a classification for apatient, the MLMO system provides the VCG 302 for the patient as aone-dimensional image. The 1D CNN 320 then classifies the VCG based onthe weights and outputs the classification, such as rotor location.

CNNs are a type of neural network that has been developed specificallyto process images. A CNN may be used to input an entire image and outputa classification of the image. For example, a CNN can be used toautomatically determine whether a scan of a patient indicates thepresence of an anomaly (e.g., tumor). The MLMO system considers thederived EM data to be a one-dimensional image. A CNN has multiple layerssuch as a convolution layer, a rectified linear unit (“ReLU”) layer, apooling layer, a fully connected (“FC”) layer, and so on. Some morecomplex CNNs may have multiple convolution layers, ReLU layers, poolinglayers, and FC layers.

A convolution layer may include multiple filters (also referred to askernels or activation functions). A filter inputs a convolution windowof an image, applies weights to each pixel of the convolution window,and outputs an activation value for that convolution window. Forexample, if the image is 256 by 256 pixels, the convolution window maybe 8 by 8 pixels. The filter may apply a different weight to each of the64 pixels in a convolution window to generate the activation value alsoreferred to as a feature value. The convolution layer may include, foreach filter, a node (also referred to as a neuron) for each pixel of theimage assuming a stride of one with appropriate padding. Each nodeoutputs a feature value based on a set of weights for the filter thatare learned during a training phase for that node. Continuing with theexample, the convolution layer may have 65,536 nodes (256*256) for eachfilter. The feature values generated by the nodes for a filter may beconsidered to form a convolution feature map with a height and width of256. If an assumption is made that the feature value calculated for aconvolution window at one location to identify a feature orcharacteristic (e.g., edge) would be useful to identify that feature ata different location, then all the nodes for a filter can share the sameset of weights. With the sharing of weights, both the training time andthe storage requirements can be significantly reduced. If each pixel ofan image is represented by multiple colors, then the convolution layermay include another dimension to represent each separate color. Also, ifthe image is a 3D image, the convolution layer may include yet anotherdimension for each image within the 3D image. In such a case, a filtermay input a 3D convolution window.

The ReLU layer may have a node for each node of the convolution layerthat generates a feature value. The generated feature values form a ReLUfeature map. The ReLU layer applies a filter to each feature value of aconvolution feature map to generate feature values for a ReLU featuremap. For example, a filter such as max(0, activation value) may be usedto ensure that the feature values of the ReLU feature map are notnegative.

The pooling layer may be used to reduce the size of the ReLU feature mapby downsampling the ReLU feature map to form a pooling feature map. Thepooling layer includes a pooling function that inputs a group of featurevalues of the ReLU feature map and outputs a feature value. For example,the pooling function may generate a feature value that is an average ofgroups of 2 by 2 feature values of the ReLU feature map. Continuing withthe example above, the pooling layer would have 128 by 128 poolingfeature map for each filter.

The FC layer includes some number of nodes that are each connected toevery feature value of the pooling feature maps. For example, if animage is to be classified as being a cat, dog, bird, mouse, or ferret,then the FC layer may include five nodes whose feature values providescores indicating the likelihood that an image contains one of theanimals. Each node has a filter with its own set of weights that areadapted to the type of the animal that the filter is to detect.

In the following, the MLMO system is described in reference to thefollowing data structures. The brackets indicate an array. For example,VCG[2].V[5].x represents the voltage for the x-axis for the fifth timeinterval in the second VCG. The data structures are further describedbelow when first referenced.

VCG Data Structures

VCG[ ]    size    V[ ]       x       y       z nVCG    V[ ]       x      y       z

Cycles Data Structure

#C C[ ]    start    end

Training Data Structure

#TD TD[ ]    nVCG[ ]    label(s)

FIG. 4 is a flow diagram that illustrates detailed processing of thegenerate classifier component of the MLMO system in some embodiments.The generate classifier component 400 is invoked to generate aclassifier. In block 401, the component invokes a generate simulatedVCGs component to simulate VCGs (VCG[ ]) for a variety of parametersets. In blocks 402-405, the component loops, generating the trainingdata for each simulation. In block 402, the component sets an index i to1 for indexing the parameter sets. In decision block 403, if index i isequal to the number of parameter sets, then all the training data hasbeen generated and the component continues at block 406, else thecomponent continues at block 404. In block 404, the component invokes agenerate training data component, passing an indication of the indexedparameter set. In block 405, the component increments index i and thenloops to block 403. In block 406, the component invokes a trainclassifier component to train the classifier based on the generatedtraining data and then completes.

FIG. 5 is a flow diagram that illustrates the processing of a generatesimulated VCGs component of the MLMO system in some embodiments. Thegenerate simulated VCGs component 500 is invoked to generate a simulatedVCG for each parameter set. In block 501, the component sets an index ito 1 for indexing through the parameter sets. In decision block 502, ifindex i is greater than the number of parameter sets, then the componentcompletes, else the component continues at block 503. In block 503, thecomponent sets an index j to 1 for indexing through the simulationsteps. In decision block 504, if index j is greater than the number ofsimulation steps, then the simulation for the indexed parameter set iscomplete and the component continues at block 507, else the componentcontinues at block 505. In block 505, the component applies thecomputational model based on the indexed parameter set and the indexedsimulation step to generate a voltage solution (VS[j]) for the indexedsimulation step. In block 506, the component increments index j and thenloops to block 504 to process the next simulation step. In block 507,the component generates a VCG (VCG[i]) for the indexed parameter setfrom the voltage solution (VS[ ]) that was calculated for the parameterset. In block 508, the component increments index i and then loops toblock 502 to process the next parameter set.

FIG. 6 is a flow diagram that illustrates the processing of a generatetraining data component for cycles of the MLMO system in someembodiments. The generate training data component 600 is invoked,passing an index i, that indexes a VCG generated for a parameter set andgenerates the training data from the VCG. In block 601, the componentinvokes an identify cycles component, passing an indication of theindexed VCG (VCG[i]) and receiving a normalized VCG (nVCG[ ]) for eachcycle along with a count (#C) of the cycles that were identified. Inblock 602, the component sets an index k to 1 for indexing through thecycles. In decision block 603, if index k is greater than the count ofthe cycles, then the training data for all the cycles of the indexed VCGhas been generated and the component completes, else the componentcontinues at block 604. In block 604, the component increments a runningcount (#TD) of the training data (TD) that is used as an index into thetraining data. In block 605, the component sets the normalized nVCG ofthe indexed training data (TD[#TD].nVCG) to the portion of the VCGspecified by the indexed cycle. The component extracts the portion fromthe x-axis, y-axis, and z-axis as defined by the start and end points ofthe cycle. In block 606, the component sets the label(s) of the indexedtraining data based on the function of the indexed parameter set (e.g.,rotor location). In block 607, the component increments index k to indexto the next cycle and then loops to block 603 to process the next cycle.

FIG. 7 is a flow diagram that illustrates the processing of an identifycycles component of the MLMO system in some embodiments. The identifycycles component 700 is invoked to identify the cycles within a VCG andprovides the normalized VCGs (nVCG[ ]) for the cycles. In block 701, thecomponent initializes an index j to 2 for indexing through the VCG andsets an index k to 0 for indexing through the identified cycles. Indecision block 702, if index j is greater than the size of the VCG, thenthe component has identified all the cycles and the component completes,providing the normalized nVCG, else the component continues at block703. In block 703, if the prior voltage of the x-axis of the VCG(VCG.V[j−1].x) is greater than or equal to zero and the indexed voltageof the x-axis of the VCG (VCG.V[j].x) is less than zero (i.e., anegative crossing of the x-axis), then the start of a possible cycle hasbeen identified and the component continues at block 704 to identify thecycle, else the component continues at block 709. In block 704, thecomponent sets the start of the indexed cycle (C[k].start) equal toindex j. In decision block 705, if at least one cycle has already beenidentified, then the end of the prior cycle is known and the componentincrements index k and continues at block 706, else the componentincrements index k and continues at block 709. In block 706, thecomponent sets the end of the prior cycle to index j−1. In block 707,the component extracts the VCG (eVCG) for the prior indexed cycledelimited by the start and the end of the prior cycle. In block 708, thecomponent invokes a normalize cycle component, passing an indication ofthe extracted VCG (eVCG), and receives the normalized cycle (nVCG). Inblock 709, the component increments the index j for indexing through theVCG and loops to block 702.

FIG. 8 is a block diagram that illustrates the processing of a normalizecycle component of the MLMO system in some embodiments. The normalizecycle component 800 is invoked, passing an indication of the VCG of acycle, and normalizes the cycle. In block 801, the component identifiesthe maximum vector magnitude V′ of the vectors in the cycle. Forexample, a vector magnitude of a vector may be calculated by taking thesquare root of the sum of the squares of the x, y, and z values of thevector. In block 802, the component sets index i to index a next axis ofthe VCG. In decision block 803, if all the axes have already beenselected, then the component completes, providing the normalized VCG,else the component continues at block 804. In block 804, the componentinitializes an index j to 1 for indexing through the vectors of anormalized cycle. In decision block 805, if index j is greater than thenumber of vectors of a normalized cycle, then the component loops toblock 802 to select the next axis, else the component continues at block806. In block 806, the component sets the normalized VCG for the indexedvector for the indexed axis to an interpolation of the passed VCG, theindexed vector, and the maximum vector magnitude V′. The interpolationeffectively compresses or expands the VCG to the number of vectors inthe normalized VCG and divides the x, y, and z values of the vector bythe maximum vector magnitude V′. In block 807, the component incrementsthe index j and then loops to block 805.

FIG. 9 is a flow diagram that illustrates processing of a generatetraining data for a sequence of similar cycles component of the MLMOsystem in some embodiments. The generate training data for a sequence ofsimilar cycles component 900 is invoked to identify sequences of twoconsecutive cycles of the VCG indexed by the passed index i that aresimilar and generate training data based on the identified sequences ofsimilar cycles. The cycles in a sequence are similar according to asimilarity score that reflects the stability of the cycles. In block901, the component invokes the identify cycles component to identify thecycles (nVCG[ ]) for the VCG. In block 902, the component sets an indexj to 2 for indexing through the identified cycles. In decision block903, if index j is greater than the number of identified cycles, thenall the cycles have been indexed and then component completes, else thecomponent continues at block 904. In block 904, the component generatesa similarity score for the cycles indexed by j−1 and j. The similarityscore may be based on, for example, a cosine similarity, a Pearsoncorrelation, and so on. In decision block 905, if similarity score isabove a similarity score threshold (T) indicating similar cycles, then asequence of similar cycles has been identified and the componentcontinues at block 906, else the component continues at block 909. Inblock 906, the component increments a running count (#TD) of thetraining data. In block 907, the component sets the training data to thesequence of similar cycles. In block 908, the component sets the labelfor the training data to a label derived from the parameter set (PS[i])used to generate the VCG and then continues at block 909. In block 909,the component increments index i to select the next sequence of cyclesand loops to block 903.

FIG. 10 is a flow diagram that illustrates the processing of a classifycomponent of the MLMO system in some embodiments. The classify component1000 is invoked, passing a VCG derived from a patient, and outputs aclassification. In block 1001, the component invokes the identify cyclescomponent, passing an indication of the VCG, and receives the normalizedVCGs for the cycles and a count of cycles. In block 1002, the componentsets an index k to 1 for indexing through the cycles. In decision block1003, if index k is greater than the number of cycles, then thecomponent completes with the classifications, else the componentcontinues at block 1004. In block 1004, the component applies theclassifier to the indexed cycle to generate the classification. In block1005, the component increments the index and then loops to block 1003 toprocess the next cycle. A different classification (e.g., differentrotor location) may be generated for each cycle. In such a case, theoverall classification may be derived from the combination of thedifferent classifications (e.g., average of the rotor locations).

Model Library Generation System

A method and system for generating a model library of models of an EMsource within a body is provided. In some embodiments, a model librarygeneration (“MLG”) system generates models based on sourceconfigurations with configuration parameters that include anatomicalparameters and electrophysiology parameters for the EM source. Theanatomical parameters specify dimensions or overall geometry of the EMsource. When the EM source is a heart, then the models may be arrhythmiamodels based on anatomical parameters that may include any subset of thegroup consisting of thickness of a heart wall (e.g., thickness of theendocardium, myocardium, and epicardium), dimensions of chambers,diameters, ventricular orientation in the chest, torso anatomy, fiberarchitecture, the location(s) of scar, fibrosis, and pro-arrhythmiasubstrate, scar shape and so on. The geometry of a heart may be measuredwhen maximal chamber volume and minimal wall thickness and activationoccur. With normal sinus, activation occurs at the time of theend-diastolic portion of a beat. With an arrhythmia, activation mayoccur at a different time during a beat. Therefore, the MLG system mayallow the geometry to be specified at times other than the end-diastolicportion. The torso anatomy may be used tailor the EM output based on thesize, shape, composition, and so on of the torso. The electrophysiologyparameters are the non-anatomical parameters that may include any subsetof the group consisting of inflammation, edema, accessory pathways,congenital heart disease, malignancy, sites of prior ablation, sites ofprior surgery, sites of external radiation therapy, pacing leads,implantable cardioverter-defibrillator leads, cardiac resynchronizationtherapy leads, pacemaker pulse generator location, implantablecardioverter-defibrillator pulse generator location, subcutaneousdefibrillator lead location, subcutaneous defibrillator pulse generatorlocation, leadless pacemaker location, other implanted hardware (e.g.,right or left ventricular assist devices), external defibrillationelectrodes, surface ECG leads, surface mapping leads, a mapping vest,and other normal and pathophysiologic feature distributions, actionpotential dynamics, conductivities, arrhythmia source location, and soon. The configuration parameters that are selected for a simulation maybe based on the machine learning algorithm employed. For example, thefiber architecture parameter may be selected for a convolutional neuralnetwork, but not for other types of neural networks. The MLG systemgenerates source configurations from which the arrhythmia models aregenerated. For each source configuration, the MLG system generates anarrhythmia model that includes a mesh and model parameter such asvariable weights for the arrhythmia model. The MLG system generates themesh based on the anatomical parameters. After the computational mesh isgenerated, the MLG generates model parameters of the arrhythmia model atpoint within the mesh based on the electrophysiology parameters of thatsource configuration. The electrophysiology parameters control themodeling of, for example, the electromagnetic propagation at that pointbased on the electrophysiology parameters. The collection of arrhythmiamodels forms an arrhythmia model library. The MLMO system can thengenerate a modeled EM output for each arrhythmia model and use themodeled EM outputs to train a classifier. The arrhythmia model librarymay be used for other purposes such as studying the efficacy ofdifferent types of ablations.

In some embodiments, the MLG system generates sets of anatomicalparameters for the heart, with each set having a value (e.g., scalar,vector, or tensor) for each anatomical parameter. A set of anatomicalparameters specifies an anatomy of a heart. The MLG system generatessimulated anatomies that are based on a set of seed anatomies(specifying values for the dimensions of the EM source) and sets ofweights that include a weight for each seed anatomy. The seed anatomiesmay also include values for features derived from the specified valuessuch as the mass, the volume, ratios of length and width, thesphericity, and so on of a chamber. The seed anatomies may representextreme anatomies found in patients. For example, the seed anatomies maybe generated from ultrasound, computed tomography, and magneticresonance imaging scans of patients who have extreme heart conditionssuch as an enlarged right ventricle, a very thick or thin endocardium,and so on. The MLG system generates a simulated anatomy for each set ofweights. Each weight in a set of weights indicates the contribution ofthe anatomical parameters of a seed anatomy to a simulated anatomy. Forexample, if four seed anatomies are specified, then the weights may be0.4, 0.3, 0.2, and 0.1. The MLG system sets the value of each anatomicalparameter for a simulated anatomy for a set of weights to a weightedaverage of the values of each anatomical parameter of the seedanatomies. The MLG system may use various techniques for generating thesets of weights, such as defining a fixed interval (e.g., 0.001),randomly selecting weights that add to 1.0, using adesign-of-experiments technique, and so on. The MLG system may alsovalidate the simulated anatomies based on comparison of the anatomicalparameters to actual anatomical parameters of actual patients. Forexample, if the combination of a height, width, and depth of a heartchamber results in a volume that has not been seen in an actual patient(e.g., not found in a patient population), then the MLG system maydiscard that simulated anatomy as it is unlikely to appear in an actualpatient.

In some embodiments, the MLG system may employ a bootstrapping techniqueto speed up the generating of the modeled EM output based on thearrhythmia models. Since a mesh for a left ventricle may have 70,000vertices, a simulation to generate the modeled EM output for anarrhythmia model for the left ventricle would require the calculation of70,000 values per EM mesh. If the simulation is for three seconds with astep size of 1 ms, then 70×10⁶ values for the vertices would need to becalculated. If the arrhythmia model library includes a millionarrhythmia models, then the number of values that need to be calculatedwould be 70×10¹². In addition, the calculation of a single value mayinvolve many mathematical operations, and multiple values may becalculated for each vertex. To help reduce the number of values thatneed to be calculated, the MLG system effectively shares some of thevalues for the EM meshes generated for one simulation based on onearrhythmia model with another simulation that is based on anotherarrhythmia model. For example, when a simulation is started, it may takeapproximately one second before some of the values of the EM mesh havean appreciable effect on the values. For example, at the one-secondpoint in simulations, the EM meshes for simulations based on arrhythmiamodels with the same source configuration except for scar or fibrosis orpro-arrhythmic substrate location may have very similar values. Toreduce the number of values that need to be calculated, the MLG systemgroups together arrhythmia models with source configurations that arethe same or nearly the same except for their scar or fibrosis orpro-arrhythmic substrate locations. The groupings may also not factor inconduction velocity and action potential parameters. The MLG system thenruns the simulation for a representative arrhythmia model of the group(e.g., one without a scar or fibrosis or pro-arrhythmic substratelocation). To run the simulations for the other arrhythmia models, theMLG system sets the values of the initial EM meshes to the values of theEM mesh at the one-second point in the simulation for the representativearrhythmia model. The MLG system then runs the other simulations for twoseconds starting with the values of the initial EM mesh. The modeled EMoutput for each other arrhythmia model includes the EM meshes for thefirst second of the representative arrhythmia model and the EM meshesfor the two seconds of that other arrhythmia model. In this way, the MLGsystem can significantly reduce the number of values (e.g.,approximately by one-third) that need to be calculated during some ofthe simulations, which can significantly speed up the generating of themodel EM output or voltage solutions for an arrhythmia model library.

In some embodiments, the MLG system may employ other bootstrappingtechniques to speed up the generating of modeled EM output. Onebootstrapping technique may allow for rapid generation for differentconfiguration parameters such as different geometries, different actionpotentials, and different conductivity parameters. For example, for agiven focal or rotor source location and a set of set of otherconfiguration parameters, a simulation may be run for two seconds. TheEM mesh from that simulation is then modified based on a differentgeometry, or the model parameters are adjusted based on different actionpotentials or conductivity parameters. The simulation is then continued.It may take a second or so for the activation potentials to stabilizebased on the different configuration parameters. Another bootstrappingtechnique speeds up the generation for a rotor anchored to a differentscar location. For example, for a given anchoring scar location, thesimulation may be run for two seconds. After the first second, the rotormay stabilize anchored to the scar location. During the second second,sufficient modeled EM output is simulated to generate a cardiogram. TheEM mesh from that simulation is then modified to have the anchoring scarlocation moved nearby and possibly modified based on a differentgeometry. The simulation is then continued. The simulation will allowthe rotor to detach from the prior anchoring scar location and attach tothe new anchoring scar location. Once attached, the modeled EM outputfor the next second or so can be used to generate ECGs or VCGs. Also,rather than modifying the anchoring scar location, an ablation shape andpattern can be added to the EM mesh to simulate the effect of thatablation, or the configuration of the anchoring scar may be modified torapidly simulate the effect of that configuration.

In some embodiments, the MLG system may speed up the generating ofderived EM data that is derived from the modeled EM outputs generatedfor the arrhythmia models of the arrhythmia model libraries. The derivedEM data may be a VCG (or other cardiogram) generated from a modeled EMoutput (e.g., 3,000 EM meshes). The MLG system may group togetherarrhythmia models with source configurations that have similaranatomical parameters. Each arrhythmia model in a group will thus havesimilar electrophysiology parameters and different anatomicalparameters. The MLG system then runs the simulation for a representativearrhythmia model of the group. The MLG system, however, does not need torun the simulations for the other arrhythmia models in the group. Togenerate the VCG for one of the other arrhythmia models, the MLG systeminputs the modeled EM output of the representative arrhythmia model andthe anatomical parameters of the other arrhythmia model. The MLG systemthen calculates the VCG values for the other arrhythmia model based onthe values of the modeled EM output with adjustments based ondifferences in the anatomical parameters of the representativearrhythmia model and the other source configuration. In this way, theMLG system avoids running any simulations except for the representativearrhythmia model of each group of arrhythmia models.

In some embodiments, the MLG system converts the arrhythmia models basedon one polyhedral model to arrhythmia models based on another polyhedralmodel. Different finite-mesh problem solvers may be based on differentpolyhedral models. For example, one polyhedral model may be a hexahedralmodel, and another polyhedral model may be a tetrahedral model. With ahexahedral model, a mesh is filled with hexahedrons. With a tetrahedralmodel, a mesh is filled with tetrahedrons. If an arrhythmia modellibrary is generated based on a hexahedral model, that arrhythmialibrary cannot be input to a tetrahedral problem solver. A separatearrhythmia model library based on a tetrahedral model could be generatedbased on the source configurations, but it would be computationallyexpensive to do so. To reduce this computational expense of generating atetrahedral arrhythmia model library, the MLG system converts hexahedralarrhythmia models to tetrahedral arrhythmia models. To convert atetrahedral arrhythmia model, the MLG system generates a surfacerepresentation of the tetrahedral arrhythmia model, for example, basedon the surface faces of the mesh of the hexahedral arrhythmia model. TheMLG system then populates the volume formed by the surfacerepresentation with tetrahedrons to generate a tetrahedral mesh. The MLGsystem then generates a value for each vertex of the tetrahedral mesh byinterpolating the values of the vertices of the hexahedral mesh that areproximate to that vertex of the tetrahedral mesh. In this way, the MLGsystem can use an arrhythmia model library for one type of polyhedron togenerate an arrhythmia model library for another type of polyhedron andavoid the computational expense of generating the arrhythmia modellibrary for the other type of polyhedron from source configurations. TheMLG system may also convert an arrhythmia model from one polyhedralmodel to another polyhedral model for display of the electromagneticsource. For example, the MLG system may run a simulation using ahexahedral ventricular mesh and then, for display, convert to asurface-triangle ventricular mesh to provide a more realistic lookingdisplay of the ventricle.

FIG. 11 is a block diagram illustrating components of the MLG system insome embodiments. The MLG system 1100 includes a generate model librarycomponent 1101, a generate simulated anatomies component 1102, agenerate simulated anatomy component 1103, a generate sourceconfigurations component 1104, a generate model component 1105, adisplay simulated anatomy component 1106, a generate voltage solutionfor representative component 1107, a generate voltage solution for groupcomponent 1108, an approximate VCGs component 1109, and a convertpolyhedral model component 1110. The MLG system also includes a modellibrary 1111 that stores arrhythmia models, a seed anatomy library 1112,which stores seed anatomies (for example, of a heart), and a simulatedanatomy library 1113, which stores simulated anatomies (for example, ofa heart). The generate model library component controls the overallgeneration of the model library. The generate simulated anatomiescomponent generates the simulated anatomies for various sets of weightsby invoking the generate simulated anatomy component for each set ofweights. The generate source configurations component generates varioussource configurations based on the simulated anatomies and possiblevalues for electrophysiology parameters. The generate model componentgenerates a model based on a source configuration. The display simulatedanatomy component may provide an application programming interface or auser experience for specifying the sets of weights and viewing seed andsimulated anatomies. The generate voltage solution for representativecomponent generates a voltage solution for a representative sourceconfiguration of a group. The generate voltage solution for groupcomponent generates voltage solutions for source configurations in agroup based on bootstrapping using the voltage solution for arepresentative source configuration. The approximate VCGs componentgenerates an approximate VCG based on a voltage solution for similarsource configuration but with different anatomical parameters. Theconvert polyhedral model component converts a hexahedral model to atetrahedral model.

FIG. 12 is a block diagram that illustrates the generating of asimulated anatomy from seed anatomies. In this example, seed anatomies1201-1206 are used to generate a simulated anatomy 1210. Each seedanatomy has an associated weight that specifies the contribution of theseed anatomy to the simulated anatomy. The weights may sum to 1.0. Insome embodiments, rather than a single weight for each seed anatomy, theMLG system may employ a weight for each anatomy parameter of the seedgeometry. For example, the weights may include a weight for dimensionsof a heart chamber, a weight for the thickness of a heart wall, and soon. The weights for an anatomy parameter of the seed anatomies may sumto 1.0. For example, the weights of seed anatomies 1201-1206 may be 0.1,0.1, 0.1, 0.1, 0.3, and 0.3, respectively, for the dimensions of theleft ventricle and 0.2, 0.2, 0.2, 0.2, 0.1, and 0.1, respectively, forthe thickness of an endocardium, myocardium, and epicardium.Alternatively, the weights for the seed anatomies or an anatomicalparameter of the seed anatomies need not sum to 1.0.

FIG. 13 is a display page that illustrates a user experience for viewingsimulated anatomies in some embodiments. A display page 1300 includes agraphics area 1310, a weights area 1320, and an options area 1330. Thegraphics area displays a graphic of each seed anatomy of a heart and theweights along with a graphic of the simulated anatomy of the heart basedon those weights. A user can use the weights area to specify the weightof each seed anatomy. The user may use the options area to addadditional seed anatomies, delete a seed anatomy, and specify weightrules based on a user interface (not illustrated) for each option. TheMLG system may specify a data structure for storing the anatomicalparameters of a seed anatomy. A user can upload the data structure whenadding a new seed anatomy. The MLG system may also allow a user tocreate a library of seed anatomies that can be used to generatesimulated anatomies. A weight rule specifies how to generate sets ofweights for a library of simulated anatomies. For example, a weight rulemay specify to generate every combination of sets of unnormalizedweights that range between 0.0 and 1.0 in increments of 0.2 and thennormalize the weights so that the weights in each set of weights sums to1.0. Given this weight rule and six seed anatomies, 5×10⁶ sets ofweights are specified. The graphics may be generated using a 3D computergraphics software toolset such as Blender.

FIG. 14 is a flow diagram that illustrates the processing of a generatemodel library component of the MLG system in some embodiments. Thegenerate model library component 1400 is invoked to generate a modellibrary, such as an arrhythmia model library, based on seed anatomiesand, for each model, a set of weights for the seed anatomies forgenerating the anatomical parameters along with electrophysiologyparameter specifications that specify the range of values for eachelectrophysiology parameter. The component generates sourceconfigurations from combinations of the anatomical parameters andelectrophysiology parameters. In block 1401, the component invokes agenerate simulated anatomies component to generate the simulatedanatomies from the seed anatomies. In block 1402, the component invokesa generate source configurations component to generate the sourceconfigurations based on the anatomical parameters of the simulatedanatomies and the electrophysiology parameter specifications. In blocks1403-1406, the component loops, generating a model for each sourceconfiguration. In block 1403, the component selects the next sourceconfiguration i. In decision block 1404, if all the sourceconfigurations have already been selected, then the component completes,else the component continues at block 1405. In block 1405, the componentinvokes a generate model component, passing an indication of the sourceconfiguration i, to generate a model (model[i]) for that sourceconfiguration. In block 1406, the component adds the generated model tothe model library and then loops to block 1403 to select the next sourceconfiguration.

FIG. 15 is a flow diagram that illustrates the processing of a generatesimulated anatomies component of the MLG system in some embodiments. Thegenerate simulated anatomies component 1500 is invoked to generatesimulated anatomies based on seed anatomies and associated sets ofweights. In block 1501, the component selects the next set of weights i.In decision block 1502, if all the sets of weights have already beenselected, then the component completes, else the component continues atblock 1503. In block 1503, the component invokes the generate simulatedanatomy component, passing an indication of the selected set of weightsi and receiving an indication of the anatomical parameters for thesimulated anatomy. In block 1504, the component stores the anatomicalparameters and then loops to block 1501 to select the next set ofweights.

FIG. 16 is a flow diagram that illustrates the processing of a generatesimulated anatomy component of the MLG system in some embodiments. Thegenerate simulated anatomy component 1600 is invoked, passing anindication of a set of weights, and generates a simulated anatomy basedon that set of weights. In block 1601, the component selects the nextanatomical parameter i. In decision block 1602, if all the anatomicalparameters have already been selected, then the component returns anindication of the anatomical parameters for the simulated anatomy, elsethe component continues at block 1603. In block 1603, the componentinitializes the selected anatomical parameter i. In blocks 1604-1606,the component loops, adjusting the selected anatomical parameter basedon the weights and the value for that anatomical parameter for each seedanatomy. In block 1604, the component selects the next seed anatomy j.In decision block 1605, if all the seed anatomies have already beenselected, then the component loops to block 1601 to select the nextanatomical parameter, else the component continues at block 1606. Inblock 1606, the component sets the selected anatomical parameter i tothe sum of the selected anatomical parameter i and that anatomicalparameter i for the selected seed anatomy j divided by the weight forthe selected seed anatomy. The component then loops to block 1604 toselect the next seed anatomy.

FIG. 17 is a flow diagram that illustrates the processing of a generatesource configurations component of the MLG system in some embodiments.The generate source configurations component 1700 is invoked to generatesource configurations for use in generating a model library. A sourceconfiguration is specified by a simulated anatomy and a combination ofvalues of anatomical parameters. In block 1701, the component selects anext simulated anatomy. In decision block 1702, if all the simulatedanatomies have already been selected, then the component completes, elsethe component continues at block 1703. In blocks 1703-1707, thecomponent loops, selecting sets of anatomical parameters for theselected simulated anatomy. In block 1703, the component selects thenext value for the fiber architecture anatomical parameter. In decisionblock 1704, if all the values for the fiber architecture anatomicalparameter have already been selected for the selected simulated anatomy,then all the source configurations for the selected simulated anatomyhave been generated and the component loops to block 1701 to select thenext simulated anatomy, else the component continues to select a valuefor each of the other anatomical parameters, as illustrated by theellipsis. In block 1705, the component selects the next source locationfor the selected simulated anatomy and set of values for the otheranatomical parameters. In decision block 1706, if all the sourcelocations have already been selected for the selected simulated anatomyand set of values for the other anatomical parameters, then thecomponent loops to block 1705 to select a new set of values. In block1707, the component stores an indication of the selected simulatedanatomy and the set of selected values for the anatomical parameters asa source configuration and then loops to block 1705 to select the nextsource location.

FIG. 18 is a flow diagram that illustrates the processing of a generatemodel component of the MLG system in some embodiments. The generatemodel component 1800 is invoked, passing an indication of a sourceconfiguration i, and generates a model. The model includes one or moremodel parameters for each vertex of a mesh representing the simulatedanatomy of the source configuration. In block 1801, the componentgenerates the mesh based on the anatomical parameters. The mesh may berepresented by a data structure that includes, for each vertex, areference to its adjacent vertices explicitly or implicitly along with avalue for one or more model parameters for use in generating, forexample, a voltage for that vertex. In block 1802, the component selectsthe next vertex j. In decision block 1803, if all the vertices havealready been selected, then the component completes, else the componentcontinues at block 1804. In block 1804, the component selects the nextmodel parameter k of the computational model for the selected vertex. Indecision block 1805, if all the model parameters have already beenselected, then the component loops to block 1802 to select the nextvertex, else the component continues at block 1806. In block 1806, thecomponent calculates the selected model parameter k for the selectedvertex j based on the source configuration i and then loops to block1804 to select the next model parameter.

FIG. 19 is a flow diagram that illustrates the processing of a displaysimulated anatomy component of the MLG system in some embodiments. Thedisplay simulated anatomy component 1900 is invoked to display asimulated anatomy that has been generated based on seed anatomies. Inblock 1901, the component displays a simulated representation of asimulated anatomy, such as graphic 1311. In blocks 1902-1906, thecomponent loops, displaying a seed representation of each seed anatomy.In block 1902, the component selects the next seed anatomy that was usedto generate the simulated anatomy. In decision block 1903, if all theseed anatomies have already been selected, then the component continuesat block 1907, else the component continues at block 1904. In block1904, the component displays a seed representation of the selected seedanatomy, such as graphics 1301-1306. In block 1905, the componentdisplays an arrow from the seed representation to the simulatedrepresentation. In block 1906, the component displays the weightassociated with the seed anatomy adjacent to the arrow and then loops toblock 1902 to select the next seed anatomy. In decision block 1907, if auser indicates to change the weight, then the component continues atblock 1908, else the component completes. In block 1908, the componentdisplays a new simulated representation of a simulated anatomy that isbased on the changed weight. The component may invoke the generatesimulated anatomy component to generate the simulated anatomy and acomponent to generate the graphic for the simulated anatomy. In block1909, the component displays the changed weight near the arrow from theseed representation of the seed anatomy whose weight has been changedand then completes.

FIG. 20 is a block diagram that illustrates the process of bootstrappinga simulation based on an EM mesh of a prior simulation with similaranatomical parameters. The simulation may be bootstrapped based on aprior simulation that in turn is based on an arrhythmia model for thesame source configuration except for different scar or fibrosis orpro-arrhythmic substrate locations. To generate the initial EM mesh, agenerate simulation component 2020 inputs an arrhythmia model 2010 witha first scar or fibrosis or pro-arrhythmic substrate location andoutputs a voltage solution 2030. The generate simulation component 2020includes an initialize mesh to default component 2021 and a runsimulation component 2022. The initialize EM mesh component initializesan EM mesh with default values for the voltages of the vertices. The runsimulation component 2022 runs a simulation (e.g., for 3 seconds) basedon the initialized EM mesh and the arrhythmia model 2010 to generate avoltage solution (e.g., 3,000 EM meshes) and outputs the voltagesolution 2030. To bootstrap a simulation, a generate simulationcomponent 2060 inputs an arrhythmia model 2050 with a second scar orfibrosis or pro-arrhythmic substrate location and outputs a voltagesolution 2070. The generate simulation component 2060 includes aninitialize EM mesh based on voltage solution component 2061 and a runsimulation component 2062. The initialize EM mesh based on voltagesolution component 2061 inputs the voltage solution 2030 and initializesthe EM mesh to, for example, the EM mesh corresponding to the one-secondpoint of the voltage solution 2030. The run simulation component 2062runs a simulation based on the initialized EM mesh. For example, if thesimulations are three seconds and the initialized EM mesh is based on anEM mesh at the one-second point, then the run simulation component 2062runs a simulation for two additional seconds. The run simulationcomponent 2062 stores the voltage solution in a voltage solution store2070.

FIG. 21 is a flow diagram that illustrates the processing of a componentto generate a voltage solution for a representative arrhythmia model ofa group of the MLG system in some embodiments. The generate voltagesolution for representative component 2100 generates a voltage solutionfor an arrhythmia model i. In block 2101, the component initializes anindex j to track the number of simulation steps. In decision block 2102,if the index j is greater than the number of simulation steps, then thecomponent completes, indicating the voltage solution, else the componentcontinues at block 2103. In block 2103, the component applies thecomputational model based on the arrhythmia model and the selectedsimulation step to generate a voltage solution for the selectedsimulation step. In block 2104, the component increments to the nextsimulation step and loops to block 2102.

FIG. 22 is a flow diagram that illustrates the processing of a generatevoltage solution component for a group of arrhythmia models based on arepresentative voltage solution of the MLG system in some embodiments.The generate voltage solution for group component 2200 is passed anindication of a representative voltage solution and a set of arrhythmiamodels. In block 2201, the component selects the next arrhythmia modeli. In decision block 2202, if all the arrhythmia models have alreadybeen selected, then the component completes, else the componentcontinues at block 2203. In block 2203, the component initializes thevoltage solution for the selected arrhythmia model to the representativevoltage solution for the first 1000 simulation steps. In blocks2204-2206, the component loops, running the simulation starting atsimulation step 1001 and continuing to the end of the simulation. Inblock 2204, the component selects the next simulation step j, startingat simulation step 1001. In decision block 2205, if the currentsimulation step is greater than the number of simulation steps, then thecomponent loops to block 2201 to select the next arrhythmia model, elsethe component continues at block 2206. In block 2206, the componentapplies the arrhythmia model based on the simulation step to generate avoltage solution for the simulation step and then loops to block 2204 toselect the next simulation step.

FIG. 23 is a block diagram that illustrates the process of approximatinga vectorcardiogram based on a voltage solution for an arrhythmia modelwith different anatomical parameters. To generate the voltage solutionfor an arrhythmia model 2301 based on a first set of anatomicalparameters, a generate simulation component 2302 inputs the arrhythmiamodel and outputs a voltage solution 2303. A generate VCG component 2304then generates a VCG from the voltage solution and stores it in a VCGstore 2305. To approximate a VCG for an arrhythmia model based on asimilar source configuration but with different anatomical parameters,an approximate VCG component 2314 inputs an arrhythmia model 2311,generates an approximate VCG, and outputs the VCG 2315.

FIG. 24 is a flow diagram that illustrates the processing of anapproximate VCG component of the MLG system in some embodiments. Theapproximate VCG component 2400 inputs a voltage solution (“VS1”)generated based on a first arrhythmia model with first anatomicalparameters and approximates a VCG for a second arrhythmia model withsecond anatomical parameters. In block 2401, the component sets an indexi for indexing through the simulation steps. In decision block 2402, ifindex i is greater than the number of simulations steps, then thecomponent continues at block 2408, else the component continues at block2403. In block 2403, the component sets an index j for indexing throughthe points (e.g., vertices or Gaussian points) of the mesh of anarrhythmia model. In decision block 2404, if index j is greater than thenumber of points, then the component continues at block 2407, else thecomponent continues at block 2405. In block 2405, the component sets thevalue for the voltage solution (“VS2”) for the second arrhythmia modelat the index simulation step and point to the corresponding value forthe voltage solution of the first arrhythmia model. In block 2405, thecomponent increments index j and loops to block 2404. In block 2407, thecomponent increments index i and loops to block 2402. In block 2408, thecomponent generates the VCG for the second arrhythmia model based on thegenerated voltage solution VS2 and then completes.

FIG. 25 is a block diagram that illustrates the process of converting anarrhythmia model based on a first polyhedron to an arrhythmia modelbased on a second polyhedron in some embodiments. A first polyhedronarrhythmia model of a first polyhedron arrhythmia model 2501 is input toan extract surface component 2502. The extract surface componentextracts the surface of the mesh of the arrhythmia model. A populatewith second polyhedrons component 2303 inputs the surface and populatesthe volume within the surface with the second type of polyhedrons. Aninterpolate model parameters component 2504 inputs the second polyhedronmesh and the first polyhedral model and generates model parameters ofthe second polyhedral model and outputs the second polyhedral model2505.

FIG. 26 is a flow diagram that illustrates the processing of a convertpolyhedral model component of the MLG system in some embodiments. Aconvert polyhedral model component 2600 is invoked to convert ahexahedral model to a tetrahedral model. In block 2601, the componentextracts the surface from the hexahedral model. In block 2602, thecomponent generates a tetrahedral mesh based on the surface of thehexahedral model. In block 2603, the component maps the vertices of thetetrahedral mesh to the space of the hexahedral mesh of the hexahedralmodel, for example, setting the locations of the vertices relative tothe same origin. In block 2604, the component selects the next vertex jof the tetrahedral mesh. In decision block 2605, if all such verticeshave already been selected, then the component completes, else thecomponent continues at 2606. In block 2606, the component selects thenext model parameter k. In decision block 2607, if all the modelparameters have already been selected for the selected vertex, then thecomponent loops to block 2604 to select the next vertex, else thecomponent continues to block 2608. In block 2608, the component sets theselected model parameter based on interpolation of the values of theselected model parameter of neighboring vertices in the hexahedralmodel. The component then loops to block 2606 to select the next modelparameter.

Patient Matching System

Methods and systems for identifying attributes or classification of anEM source within a body based on patient matching that is matching apatient source configuration to model source configurations of the EMsource are provided. In some embodiments, a patent matching (“PM”)system uses the matching model source configurations to speed up theprocess of identifying the attributes for the patient or to provide amore accurate classification for the patient using a classifier. Toidentify an attribute (e.g., arrhythmia source location) for a patient,the PM system generates a mapping of each model source configurationused to generate simulated (or modeled) EM output to the derived EM datathat is derived from the simulated EM output generated based on thatmodel source configuration. Each derived (or modeled) EM data is alsomapped to an attribute associated with the EM source having the modelsource configuration. For example, when the EM source is a heart, themodel source configurations include configuration parameters such asanatomical parameters and electrophysiology parameters. Theconfiguration parameters may include the source location of anarrhythmia, a rotor type, type of disorder or condition, and so on. Oneof the configuration parameters, such as arrhythmia source location, maybe designated as an attribute parameter, which represents the attributeto be identified for a patient. The PM system generates the mappings ofmodel source configurations to modeled VCGs and generates the mappingsof modeled VCGs to their corresponding source location of thearrhythmia. The mappings of modeled VCGs to source locations may begenerated in a manner similar to how the training data is generated bythe MLMO system with the attribute corresponding to the label.

In some embodiments, the PM system uses the mappings to identify theattribute for a patient based on a combination of the patient sourceconfiguration of the patient and a patient VCG of the patient. Theattribute can be identified by comparing the patient VCG to each modeledVCG to identify the most similar modeled VCG and by identifying theattribute of that most similar modeled VCG as the patient attribute ofthe patient. It can, however, be computationally very expensive tocompare the patient VCG to each modeled VCG because the number ofmodeled VCGs may be in the millions. The PM system may employ varioustechniques to reduce the computational expense. With one technique, thePM system uses the patient source configuration to reduce the number ofmodeled VCGs to which the patient VCG needs to be compared. The PMsystem may allow a user to provide the patient source configuration fora patient. It would be preferable if a value for each configurationparameter of the patient source configuration could be provided. Inpractice, however, it may be that the values of only certainconfiguration parameters are known for a patient. In such cases, the PMsystem performs the comparison based on the known values of theconfiguration parameters, rather than all the configuration parameters.The anatomical parameters for a patient may be calculated based onimaging scans. The action potentials may be calculated based on outputof a basket catheter inserted into the patient's heart or the patient'shistory of anti-arrhythmic drug or gene therapy, and the conductivitymay be calculated based on analysis of the patient's ECG. Theconfiguration parameters may also include electrophysiology parametersto indicate whether the action potential and/or conductivity representsa diseased state. In such a case, if the action potential orconductivity of a patient is not available, the configuration parametersindicate whether the action potential or conductivity represent adisease state. The PM system may use various techniques to assess thesimilarity between a patient source configuration and a model sourceconfiguration such as one based on least squares, cosine similarity, andso on. The PM system may generate a similarity score for each modelsource configuration and identify as matching model sourceconfigurations those with a similar score that is above a matchingthreshold.

In some embodiments, after the matching model source configurations areidentified, the PM system compares the patient VCG to the modeled VCGsto which the matching model source configurations are mapped. The PMsystem may generate a similar score for each modeled VCG (e.g., using aPearson correlation technique) and identify as matching modeled VCGsthose with a similarity score above a threshold similarity. The PMsystem then identifies the attribute(s) for the patient based on theattributes of the matching modeled VCGs. For example, if the attributeis source location of an arrhythmia, the PM system may generate anaverage of the source locations weighted based on the similar scores ofthe matching model VCGs. In this way, the PM system avoids thecomputational expense of comparing a patient VCG to every modeled VCG.

In some embodiments, the PM system may employ other techniques forreducing the computational expense of comparing the patient VCG to everymodeled VCG. For example, the PM system may identify features of VCGssuch as area, maximum dimensions, and so on. The PM system then maygenerate an index that maps values of the features to the modeled VCGshaving those values. To identify modeled VCGs that match a patient VCG,the PM system identifies the features of the patient VCG and uses theindex to identify matching modeled VCGs. For example, the PM system mayidentify for, each feature, a set of the modeled VCGs that match thatfeature of the patient VCG. The PM system can then identify the modeledVCGs that are common to each set (e.g., intersection of the sets) ormost common to the sets as the matching modeled VCGs. The PM system canthen compare those matching modeled VCGs to the patient VCG as describedabove to identify the attribute.

In some embodiments, the PM system may employ patient matching toimprove the classification of VCGs based on a trained classifier. The PMsystem may generate clusters of similar model source configurationsusing various clustering techniques. The clustering techniques mayinclude a centroid-based clustering technique (e.g., k-meansclustering), a supervised or unsupervised learning clustering technique(e.g., using a neural network), and so on. With a centroid-basedclustering technique, the PM system generates clusters by successivelyadding each model source configuration to the current cluster of modelsource configurations to which it is most similar. The PM system maycombine and split clusters dynamically, for example, based on the numberof model source configuration in each cluster, the similarity of themodel source configurations of one cluster to another, and so on. Afterthe clusters are identified, the PM system may employ components of theMLMO system to generate a classifier for each cluster. The PM systemtrains the classifier for a cluster based on the modeled VCGs for themodel source configurations of that cluster as the training data for theclassifier.

In some embodiments, to generate a classification for a patient, the PMsystem identifies the cluster with model source configurations that bestmatches the patent source configuration. For example, the PM system maygenerate a similarity score for each cluster based on similarity betweena representative model source configuration (e.g., with average valuesof a cluster) of that cluster and the patient source configuration. ThePM system then selects the classifier for the cluster with the highestsimilarity score and applies that classifier to the patient VCG togenerate the classification for the patient. Because each classifier istrained based on a cluster of similar model source configurations, eachclassifier is adapted to generate classifications based only on thedifferences, which may be subtle, in those similar model sourceconfigurations. In contrast, a classifier trained based on all modelsource configurations may not be able to factor in subtle differencesbetween similar model source configurations. As such, a classifiertrained based on a cluster of similar model source configurations mayprovide a more accurate classification than a classifier trained basedon all model source configurations.

In some embodiments, the PM system may generate the modeled EM outputfor the model source configurations assuming a standard orientation of aheart. If a patient, however, has a heart with a somewhat differentorientation, then the matching modeled VCGs may not have attributes orclassifications that would apply to the patient because of thedifferences in the orientations. In such a case, the PM system mayperform a VCG rotation prior to comparing the patient VCG to the modeledVCGs. The PM system may rotate either each modeled VCG or the patientVCG. The PM system may generate a rotation matrix based on thedifference in orientations. The PM system then performs a matrixmultiplication between each point (e.g., x, y, and z values) of a VCGand the rotation matrix (e.g., a 3-by-3 matrix) to generate a rotatedpoint for the rotated VCG. The MLMO system may also rotate VCGs based ondifference in orientations.

FIGS. 27-30 are flow diagrams that illustrates the processing ofcomponents of the PM system in some embodiments. FIG. 27 is a flowdiagram that illustrates the processing of an identify attributescomponent of the PM system in some embodiments. The identify attributescomponent 2700 identifies patient attributes of a patient based on apatient source configuration and a patient cardiogram (e.g., the VCG).In block 2701, the component invokes the generate simulated VCGscomponent of the MLMO system to generate the modeled VCGs and then mapsthem to the model source configurations from which they were generated.In block 2702, the component retrieves the patient source configurationand the patient VCG. In block 2703, the component invokes the identifymatching VCGs component passing an indication of the patient sourceconfiguration and the patient VCG to identify patient attributes basedon the matching VCGs. In block 2704, the component presents the patientattributes to inform treatment on the patient and then completes.

FIG. 28 is a flow diagram that illustrates the processing of an identifymatching VCGs component of the PM system in some embodiments. Theidentify matching VCGs component 2700 is passed a patient sourceconfiguration and a patient VCG and identifies the patient attributes.In block 2801, the component selects the next mapping of a model sourceconfiguration to a modeled VCG. In decision block 2802, if all themappings have already been selected, then the component completesindicating the patient attributes, else the component continues at block2803. In decision block 2003, if the selected model source configurationmatches the patient source configuration, then the component continuesat block 2804, else the component loops to block 2801 to select the nextmapping. In decision block 2804, if the modeled VCG for the selectedmapping matches the patient VCG, then the component continues at block2805, else the component loops to block 2801 to select the next mapping.In block 2805, the component adds the attribute for the selected modeledVCG to the patient attributes and then loops to block 2801 to select thenext mapping. In some embodiments, the PM system may include componentsto identify other types of matching measurements such as an EEG andmeasurements corresponding to those collected by a body surface vest, abasket catheter, a cap worn by a patient, and so on.

FIG. 29 is a flow diagram that illustrates the processing of an identifyclasses based on clustering component of the PM system in someembodiments. The identify classes based on clustering component 2900identifies the classes for a patient based on classifiers trained usingclusters of similar model source configurations. In block 2901, thecomponent invokes a generate cluster classifiers component to generateclassifiers for clusters of similar model source configurations. Inblock 2902, the component receives a patient source configuration and apatient VCG. In blocks 2903-2908, the component loops selecting clustersto identify the cluster with model source configurations that are mostsimilar to the patient source configuration. In block 2903, thecomponent initializes the variable to track the maximum similaritycalculated so far. In block 2904, the component selects the nextcluster. In decision block 2905, if all the clusters have already beenselected, then the component continues at block 2909, else the componentcontinues at block 2906. In block 2906, the component invokes acalculate similarity component to calculate the similarity between themodel source configurations of the selected cluster and the patientsource configuration. In decision block 2907, if the similarity isgreater than the maximum similarity calculated so far, then thecomponent continues at block 2908, else the component loops to block2904 to select the next cluster. In block 2908, the component sets themaximum similarity to the similarity calculated for the selected clusterand sets a variable to indicate the index of the cluster with themaximum similarity calculated so far. The component then loops to block2904 to select the next cluster. In block 2909, the component invokes aclassify based on cluster component to identify the classes for thepatient VCG based on the cluster with the maximum similarity. Theclassify based on cluster component may correspond to the classifycomponent of the MLMO system that has been adapted to input theclassifier to use. The component then completes indicating the classes.

FIG. 30 is a flow diagram that illustrates the processing of a generatecluster classifiers component of the PM system in some embodiments. Thegenerate cluster classifiers component 3000 is invoked to cluster themodel source configurations and generate a classifier based on eachcluster. In block 2001, the component generates the clusters of themodel source configurations. In block 3002, the component selects thenext cluster. In decision block 3003, if all the clusters have alreadybeen selected, then the component completes indicating the classifiers,else the component continues at block 3004. In block 3004, the componentinvokes a generate classifier component of the MLMO system passing anindication of the model source configurations of the selected cluster togenerate a classifier for that cluster. The component then loops toblock 3002 to select the next cluster.

Machine Learning Based on Clinical Data

Methods and systems are provided to adapt the MLMO system to generateclassifiers based on actual patient data. In some embodiments, a machinelearning based on clinical data (“MLCD”) system is provided thatgenerates (1) a patient classifier based on patient training dataassociated with actual patients using transference of model classifierweights and (2) generates a patient-specific model classifier based onmodel training data that is selected based on similarity to a patient.The patient classifier is generated by a patient classifier system ofthe MLCD system, and patient-specific model classifier is generated by apatient-specific model classifier system. The term “patient classifier”refers to a classifier that is generated based on patient training datagenerated based on data of patients, and the term “model classifier”refers to a classifier that is generated based on model training datagenerated based on a computational model of an EM source.

Patient Classifier System

In some embodiments, the patient classifier (“PC”) system generates apatient classifier for classifying derived EM data derived from EMoutput of an EM source within a body. For example, the patent classifierclassifies VCGs derived from ECGs. The PC system accesses a modelclassifier such as one generated using the MLMO system. The modelclassifier is generated using model training data generated using acomputational model of an EM source. The model classifier includes modelclassifier weights that are learned when the model classifier is trainedsuch as the weights of the activation functions of a CNN. The PC systemalso accesses patient training data that includes, for each of apatient, patient derived EM data (e.g., VCGs) and a patientclassification for that patient such as rotor location and priorablation procedure outcome. An example prior ablation procedure outcomemay be that a patient was arrhythmia free for a certain time periodafter being ablated at a certain location with a certain burn pattern.To train the patient classifier, the PC system initializes patientclassifier weights of the patient classifier based on the modelclassifier weights of the model classifier and then trains the patientclassifier with the patient training data and with the initializedpatient classifier weights. Normally, the weights of a classifier areinitialized to default values such as all to a certain value (e.g., 0.0,0.5, or 1.0) or to random values. The learning of the weights for theclassifier is thus considered to be from “scratch.” The process ofinitializing the values of the weights based on previously learnedweights is referred to as “transference” of knowledge. The knowledgegained by the previous training of a prior classifier is transferred tothe training of a new classifier. The goal of transference is to bothspeed up the training of and increase the accuracy of the newclassifier.

In some embodiments, the PC system uses the patient classifier toclassify patients. For example, when the EM source is a heart, the PCsystem may receive a cardiogram (e.g., ECG or VCG) of a patient andapply the patient classifier to that cardiogram. Depending on thepatients selected for training the patient classifier, the patientclassifier may be a more accurate classifier than a model classifiertrained on model training data generated using a computational model.Moreover, if the patient is similar to the patients used to train thepatient classifier, then the accuracy of the classifier may be even moreaccurate. The PC system may also identify clusters of similar patientsand train a separate patient classifier for each cluster, referred to asa cluster patient classifier. The similarity of the patients may bedetermined in various ways such as based on comparison of variouscharacteristics such one or more of derived EM data (e.g., cardiograms)collected from the patients, patient source configurations of thepatients (e.g., anatomical parameters, electrical dynamic properties),patient demographic information, and so on. The cluster patientclassifier for each cluster may be trained based on the VCGs andcorresponding labels of the patients in that cluster. When a targetpatient is to be classified, the PC system identifies the cluster ofpatients to which the target patient is most similar. The PC system thenapplies the cluster patient classifier of the identified cluster to VCGof the target patient.

Patient-Specific Model Classifier System

In some embodiments, a patient-specific model classifier (“PSMC”) systemgenerates a patient-specific model classifier for classifying derived EMdata of an EM source within a body. The PSMC system identifies modelsthat are similar to a patient. The PSMC system identifies similar modelsbased on a patient-model similarity. The patient-model similarity may bebased on similarity between model source configuration of a model andpatient source configuration of the patient and/or similarity betweenmodeled derived EM data (e.g., VCGs) of a model and correspondingpatient derived EM data of the patient. For example, the PSMC system maybase the similarity on anatomical parameters (e.g., dimensions of aright ventricle), certain electrophysiology parameters, and so on. ThePSMC system then may use the MLMO system to generate a patient-specificmodel classifier using the model source configurations of the similarmodels. The PSMC system may generate the patent-specific modelclassifier by first applying a computational model of the EM source togenerate modeled EM output of the EM source based on the model sourceconfigurations of the similar models. The PSMC system then generatesmodel training data that includes modeled derived EM data (e.g., VCGs)from the generated modeled EM output and labels for the models.Alternatively, if the model training data has already been generated forthe similar models, then the PSMC system need not regenerate the modeltraining data. The PSMC system then trains the patient-specific modelclassifier based on the model training data.

In some embodiments, after a patient-specific model classifier isgenerated, the PSMC system applies the patient-specific model classifierto derived EM data (e.g., VCG) of the patient to generate aclassification for the patient. Because the patient-specific modelclassifier is trained using model training data that is selected basedon the patient, the patient-specific model classifier providesclassification that is more accurate than a classification that would beprovided by a model classifier trained based on a collection of modeltraining data that is not specific to the patient.

In some embodiments, the PSMC system may generate a cluster-specificmodel classifier for a cluster of target patients. To generate acluster-specific model classifier, the PSMC system identifies modelsthat are overall similar to the target patients of the cluster and thentrains a cluster-specific model classifier based on the similar models.The PSMC system can then apply the cluster-specific model classifier togenerate a classification for the target patients of the cluster. ThePSMC system may also generate clusters of target patients and generate acluster-specific model classifier for each cluster. The PSMC system canthen generate a classification for each target patient using thecluster-specific model classifier for the cluster to which that targetpatient is a member. The PSMC system may even use a cluster-specificmodel classifier to generate a classification for a new target patient.The PSMC system identifies the cluster to which a new target patient ismost similar and applies the cluster-specific model classifier for thatidentified cluster to the patient derived EM data for the new targetpatient to generate the classification for the new target patient.

FIG. 31 is a block diagram that illustrates the overall processing of apatient classifier system of a MLCD system in some embodiments.Classifier components 3110 (i.e., components 3111-19) are similar toclassifier components 110 of FIG. 1. The term “model” has been insertedin various components to emphasize that the components are used togenerate a classifier based on models represented by simulated sourceconfigurations or parameter sets. Components 3120 include a patient datastore 3121, a generate patient training data component 3122, a patienttraining data store 3123, a train patient classifier component 3124, anda patient classifier weights store 3125. The patient data store mayinclude ECGs collected from patients and corresponding labels such aslocations of a heart disorder. The generate patient training datacomponent generates patient training data from the patient data, forexample, by generating VCGs from the ECGs and labeling the VCGs andstoring the training data in the patient training data store. The trainpatient classifier component inputs the model classifier weights from amodel classifier weights store 3119 as transference of knowledge fromthe model classifier and trains the patient classifier based on thepatient training data. The train patient classifier then stores thepatient classifier weights in the patient classifier weights store.

FIG. 32 is a flow diagram that illustrates the processing of a generatepatient classifier component of the patient classifier system in someembodiments. The generate patient classifier component 3200 generates apatient classifier for a collection of patients using transference ofknowledge from a model classifier. In block 3201, the component invokesa generate classifier component to generate a model classifier based onmodel training data if the model classifier has not already beengenerated. The generate classifier component generates model classifierweights for the model classifier. In block 3202, the component extractsthe model classifier weights of the model classifier. In block 3203, thecomponent generates the patient training data, for example, bygenerating VCGs and labeling the VCGs. In block 3204, the componentinitializes the patient classifier weights of the patient classifier tothe model classifier weights. In block 3205, the component invokes atrain classifier component to train a patient classifier based on thepatient training data and the initialized patient classifier weights.The component then completes.

FIG. 33 is a flow diagram that illustrates the processing of a generatecluster patient classifier of the patient classifier system in someembodiments. The generate cluster patient classifier 3300 generates acluster patient classifier for clusters of patients. In block 3301, thecomponent generates clusters of patients, for example, based onsimilarity of their clinical features, source configurations, or VCGs.In block 3302, the component selects the next cluster. In decision block3303, if all the clusters have already been selected, then the componentcompletes, else the component continues at block 3304. In block 3304,the component invokes a generate patient classifier component passing anindication of the selected cluster to generate a cluster patientclassifier for the patients in the selected cluster and then loops toblock 3302 to select the next cluster. When the generate patientclassifier component is invoked, it does not need to generate a modelclassifier for each invocation because it can reuse the same modelclassifier weights for each invocation.

FIG. 34 is a block diagram that illustrates components of apatient-specific model classifier system of a MLCD system in someembodiments. The PSMC system includes components 3410 (components3411-19) are similar to components 110 of FIG. 1. The PSMC system alsoincludes an identify similar heart configurations component 3430 and anidentify similar VCGs component 3440, which represent two differentembodiments of the PSMC system. In a first embodiment, the identifysimilar hearts configuration component inputs a patient heartconfiguration and model heart configurations and identifies the modelheart configurations that are similar to the patient heartconfiguration. The similar model heart configurations are then input tothe generate simulations component to generate the voltage solutions andultimately train the patient-specific model classifier. In a secondembodiment, the identify similar VCGs component inputs a patient VCG andthe training data and identifies VCGs of the training data that aresimilar to the patient VCG. The training data for the similar VCGs is aninput to the train PSMC classifier component 3418 to train thepatient-specific model classifier. Although not illustrated, the trainPSMC classifier may use transference to initialize the PSMC classifierweights. Also, the first and second embodiments may be both used togenerate training data based on similar heart configurations and thenselect similar VCGs for training.

FIG. 35 is a flow diagram that illustrates processing of a generatepatient-specific model classifier component of the PSMC system in someembodiments. The generate patient-specific model component 3500generates a patient-specific model classifier for a target patient. Inblock 3501, the component invokes an identify similar models componentpassing an indication of the target patient to identify similar models.In block 3502, the component generates model training data based on theidentified similar models. The component may employ the MLMO system togenerate the training data based on the model heart configurations ofthe identified similar models or, if already generated, retrieve thetraining data based on similar VCGs. In block 3503, the componentinvokes the train classifier component to train the patient-specificmodel classifier based on the model training data for the similar modelsand then completes.

FIG. 36 is a flow diagram that illustrates the processing of an identifysimilar models component of the PSMC system in some embodiments. Theidentify similar models component 3600 identifies models that aresimilar to a patient. In block 3601, the component selects the nextmodel. In decision block 3602, if all the models have already beenselected, then the component completes indicating the similar models,else the component continues at block 3603. In block 3603, the componentgenerates a similarity score between the selected model and the patient.The patient-model similarity score may be based on heart configurations,cardiograms, or both. In decision block 3604, if the similarity score isabove a similarity threshold, then the component continues at block3605, else the component loops to block 3601 to select the next model.In block 3605, the component designates the model as similar to thepatient and loops to block 3601 to select the next model.

Patient-Specific Model Display

Methods and systems are provided to adapt the MLMO system to supportgenerating and displaying a representation of an EM source of a patientthat is based on modeled EM output for a model that is similar to thepatient. In some embodiments, a patient-specific model display (“PSMD”)system identifies a model for the EM source that is deemed similar tothe EM source of a patient. The PSMD system then generates a graphicalrepresentation of patient's EM source based on clinical parameters forthe patient such as infarction and drug history. For example, if the EMsource is a heart, the PSMD system may generate a map representing ananatomical model of the patient's heart. The PSMD system then populatesthe map with display values that are derived from the modeled EM outputfor the similar model. For example, the PSMD system may select an EMmesh of that modeled EM output and set each the value of each vertex ofthe map based on the corresponding voltage of that EM mesh. The PSMDsystem may also map the modeled EM output from the polyhedral mesh usedin the simulation to another polyhedral mesh to produce a more realisticlooking display such as a hexahedral mesh to a surface-triangle mesh.The PSMD system may set the values corresponding to high voltages tovarying intensities of the color red (or grey scale shading) and thevalues corresponding to low voltages to varying intensities of the colorgreen. As another example, the PSMD system may select a cycle of themodel EM output and set the values based on the differences or deltabetween voltages of the first EM mesh in the cycle and the last EM meshin the cycle. The PSMD system may also set the values based on anaccumulation, which may be weighted, of the deltas over successive EMmeshes in a cycle. The PSMD system then displays (e.g., using arasterization technique) the map as the representation of activity ofthe patient's EM source. The PSMD system may also display an outlinedimage of a heart based on the anatomical parameters of the patient. Theoutlined image may illustrate boundaries of the chambers of the heart.

In some embodiments, the PSMD system may identify a model that issimilar to a patient by comparing model source configurations andmodeled derived EM data to the patient source configuration and thepatent derived EM data. The comparison may be based on the processingrepresented by the processing of the identify matching VCGs componentillustrated in FIG. 28. That component, however, may be adapted togenerate a similarity score for each matching VCG so that the PSMDsystem may select the VCG, and thus the model, that is most similar tothe patient. Alternatively, the PSMD system may generate values for thepixels of the map based on a weighted combination (e.g., an average) ofthe values of the EM mesh for all the matching models. The PSMD systemmay also weight the values based on the similarity scores of thematching models.

In some embodiments, the PSMD system may generate a sequence of maps fordisplay as a video representation of activation of patient's EM sourceover time. For example, the PSMD may divide the cycle into 30 displayintervals and generate a map for each display interval based on valuesof an EM mesh corresponding to that display interval. The PSMD systemmay then display the 30 maps in sequence over the time of the cycle toprovide a video based on the actual cycle time or in sequence over atime longer than the cycle to provide a slow-motion effect. The PSMDsystem may also achieve a slow-motion effect by generating more maps persecond of simulation time than the maximum frame rate for the display.For example, if the maximum frame rate is 60 frames-per-second, thengenerating 120 maps per second of simulation will result in one secondof simulation time being displayed over two seconds. The slowest andsmoothest slow-motion effect may be achieved by generating a map fromthe voltage solution for each simulation time.

FIG. 37 is a block diagram that illustrates the overall processing of apatent-specific model display system in some embodiments. Components3710 (i.e., components 3711-15) are similar to components 111-115 ofFIG. 1. Components 3720 include a collect ECG component 3721, a generateVCG component 3722, an identify similar VCG component 3723, a generatedisplay representation component 3724, and a display device 3725. Thecollect ECG component receives an ECG for the patient whose heart is tobe represented by the output of the PSMD system. The generate VCGcomponent receives the patient's ECG and generates a VCG for thepatient. The identify similar VCG component compares the patient VCG tothe modeled VCGs of the VCG store to identify a modeled VCG that issimilar to the patient VCG. Similar VCGs may be identified based oncomparison of cycles of the VCGs. Thus, the identify similar VCGcomponent may invoke an identify cycles component to identify the cyclesof each VCG. The generate display representation component inputs thesimilar modeled VCG and generates a display representation of thepatient's heart based on the voltage solutions from which the similarmodeled VCG was derived. The generate display representation componentthen outputs the display representation to the display device.

FIG. 38 is a flow diagram that illustrates the processing of a generatepatient heart display component of the PSMD system in some embodiments.The generate patient heart display component 3800 is provided a patientVCG and a patient heart configuration and generates an outputrepresentation of the patient's heart. In block 3801, the componentinvokes an identify matching VCGs component of the patient matchingsystem passing an indication of the patient VCG and the patient heartconfiguration to identify one or more matching VCGs. In block 3802, thecomponent identifies the closest matching of the VCGs. In block 3803,the component identifies a cycle within the closest matching VCG. Inblock 3804, the component invokes the calculate display values componentpassing an indication of the identified cycle to generate display valuesfor map. In block 3805, the component generates the displayrepresentation by storing the display values in the map. In block 3805,the component display values in the map to represent an outline of thepatient's heart based on anatomical parameters of that patient. In block3807, the component outputs the map and completes.

FIG. 39 is a flow diagram that illustrates the processing of a calculatedisplay values component of the PSMD system in some embodiments. Thecalculate display values component 3900 is passed an indication of acycle and generates display values based on EM meshes of that cycle. Inblock 3901, the component selects the first voltage solution of thecycle. In block 3902, the component selects another voltage solution ofthe cycle such as the last voltage solution. In block 3903, thecomponent selects the next value of a voltage solution. In decisionblock 3904, if all the values of already been selected, then thecomponent continues at block 3906, else the component continues at block3905. In block 3905, the component sets a delta value for the selectedvalue to the difference between the value for the first voltage solutionand the last voltage solution of the cycle. The delta value mayalternatively be weight accumulation of the differences over a cycle.The component then loops to block 3903 to select the next value of thevoltage solution. In block 3906, the component selects the next displayvalue of the map. In decision block 3907, if all the display values havealready been selected, then the component completes indicating thedisplay values, else the component continues at block 3908. In block3908, the component identifies neighboring delta values that are nearthe display value. In block 3910, the component sets the display valueto a function of the neighboring delta values and then loops to block3906 to select the next display value. The function may take the averageof the neighboring delta values, a weighted average based on distancebetween the location of the voltage solution and the location and thepatient heart that the display value represents, and so on.

Display of an Electromagnetic Force

Methods and systems for generating a visual representation of anelectromagnetic force generated by an electromagnetic source within abody is provided. In some embodiments, an electromagnetic force display(“EFD”) system generates a “surface representation” of theelectromagnetic force from a sequence of vectors representing magnitudeand direction of the electromagnetic force over time. For example, whenthe electromagnetic source is a heart, the sequence of vectors may be avectorcardiogram. The vectors are relative to an origin, which may belocated within the electromagnetic source. To generate the surfacerepresentation, the EFD system, for pairs of vectors adjacent in time,identifies a region based on the origin and the pair of vectors. Forexample, if a vector has the values (1.0, 2.0, 2.0) for its x, y, and zcoordinates and the adjacent vector has values (1.1, 2.0, and 2.0), thenthe region is the area bounded by the triangle with the vertices at(0.0, 0.0, 0.0), (1.0, 2.0, 2.0), and (1.1, 2.0, and 2.0). The EFDsystem then displays a representation of each region to form the surfacerepresentation of the electromagnetic force. Since the regions willunlikely lie in one plane, the EFD system may provide shading orcoloring to help illustrate that the regions lie in different planes.The EFD system may also display a representation of the electromagneticsource so that the surface representation visually emanates from theelectromagnetic source. For example, the EFD system may display a heartthat is based on the anatomical parameters of a patient from whom theVCG was collected. When the electromagnetic force has cycles, theregions for a cycle form a surface representation for that cycle. TheEFD system may simultaneously display surface representations formultiple cycles. For example, when the electromagnetic source is aheart, the cycle may be based on an arrhythmia. The EFD system may alsodisplay the surface representation of each cycle in sequence (e.g.,centered at the same location on the display) illustrate changes in theelectromagnetic force over time. The EFD system may display each regionof a surface representation in sequence to illustrate the timesassociated with the vectors. The EFD system may be used display eithersimulated VCGs or VCGs collected from patients.

FIG. 40 illustrates various surface representations ofvectorcardiograms. Image 4010 illustrates a display of a surfacerepresentation of a vectorcardiogram based on a perspective view. Image4020 illustrates a display of a surface representation ofvectorcardiogram shown as emanating from a heart. Images 4031-34illustrate display of the surface representation over time to illustratechanges in the electromagnetic force.

In some embodiments, the EFD system may employ a variety of animationtechniques to assist a user in analyzing a VCG. For example, the EFDsystem may animate the display of the surface representation bydisplaying each region in sequence with a timing that is the same as theactual timing of the VCG. If a cycle is 1000 ms, then EFD system woulddisplay the regions in sequence over 1000 ms. When displaying thesurface representations for multiple cycles in sequence and the nextregion to display would overlap a region previously displayed, then theEFD system may first remove the region that would be overlapped and thendisplay the next region. The EFD system may also allow the user tospecify that only a portion of the surface representation for a cycle bedisplayed. For example, the user may specify to display a portioncorresponding to 250 ms of the surface representation. In such a case,the EFD system may animate the display of the portion by removing thetail region when adding a head region. The EFD system may also allow theuser to speed up or slow down the display of the surfacerepresentations.

FIG. 41 is a flow diagram that illustrates the processing of a visualizeVCG component of the EFD system in some embodiments. The visualize VCGcomponent 4100 is passed a VCG and generates a surface representationfor each portion (e.g., cycle) of the VCG. In block 4101, the componentdisplays a representation of a heart. In block 4102, the componentselects the next cycle of the VCG. In decision block 4103, if all thecycles have already been selected, then the component completes, elsethe component continues at block 4104. In block 4104, the componentinvokes a display VCG surface representation component to display asurface representation for the selected cycle and then loops to block4102 to select the next cycle.

FIG. 42 is a flow diagram that illustrates the processing of a displayVCG surface representation component of the EFD system in someembodiments. The display VCG surface representation component 4200 ispassed a vectorcardiogram and generates a surface display representationof the VCG. In block 4201, the component sets an index t to 2 forindexing through the time intervals of the VCG. In decision block 4002,if the index t is greater than the number of time intervals, then thecomponent completes, else the component continues at block 4203. Inblock 4203, the component generates a VCG triangle based on the origin,the indexed interval t, and the prior interval t−1. In block 4204, thecomponent fills the VCG triangle with a shading that may vary based onthe plane of the triangle. In block 4205, the component displays thefilled VCG triangle. In block 4206, the component increments the indext. In decision block 4207, if the index t is greater than the displayspan t_(span)+1, then the component continues at block 4208, else thecomponent loops to block 4202. In block 4208, the component removes fromthe display the VCG triangle at the beginning of the currently displayedspan and loops to block 4202.

The following paragraphs describe various embodiments of aspects of theMLMO system. An implementation of the MLMO system may employ anycombination of the embodiments. The processing described below may beperformed by a computing system with a processor that executescomputer-executable instructions stored on a computer-readable storagemedium that implements the MLMO system.

In some embodiments, a method performed by one or more computing systemsis provided for generating a classifier for classifying electromagneticdata derived from an electromagnetic source within a body. The methodaccesses a computational model of the electromagnetic source, whereinthe computational model is for modeling electromagnetic output of theelectromagnetic source over time based on a source configuration of theelectromagnetic source. For each of a plurality of sourceconfigurations, the method generates, using the computational model, amodeled electromagnetic output of the electromagnetic source for thatsource configuration. The method, for each modeled electromagneticoutput, derives the electromagnetic data for the modeled electromagneticoutput and generates a label for the derived electromagnetic data basedon the source configuration for the modeled electromagnetic data. Themethod trains a classifier with the derived electromagnetic data and thelabels as training data. In some embodiments, the modeledelectromagnetic output for a source configuration includes, for each ofa plurality of time intervals, an electromagnetic mesh with a modeledelectromagnetic value for each of a plurality of locations of theelectromagnetic source. In some embodiments, the derived electromagneticdata, for a time interval, is an equivalent source representation of theelectromagnetic output. In some embodiments, the equivalent sourcerepresentation is generated using principal component analysis. In someembodiments, the method further identifies cycles within the derivedelectromagnetic data for a modeled electromagnetic output. In someembodiments, the same label is generated for each cycle. In someembodiments, the method further identifies a sequence of cycles that aresimilar, wherein the same label is generated for each sequence. In someembodiments, the deriving of the electromagnetic data for a modeledelectromagnetic output includes normalizing the modeled electromagneticoutput on a per-cycle basis. In some embodiments, the classifier is aconvolutional neural network. In some embodiments, the convolutionalneural network inputs a one-dimensional image. In some embodiments, theclassifier is a recurrent neural network, an autoencoder, a restrictedBoltzmann machine, or other type of neural network. In some embodiments,the classifier is a support vector machine. In some embodiments, theclassifier is Bayesian. In some embodiments, the electromagnetic sourceis a heart, a source configuration represents a source location andother properties of a heart disorder, the modeled electromagnetic outputrepresents activation of the heart, and the electromagnetic data isbased on body-surface measurements such as an electrocardiogram. In someembodiments, the heart disorder is selected from a set consisting ofinappropriate sinus tachycardia (“IST”), ectopic atrial rhythm,junctional rhythm, ventricular escape rhythm, atrial fibrillation(“AF”), ventricular fibrillation (“VF”), focal atrial tachycardia(“focal AT”), atrial microreentry, ventricular tachycardia (“VT”),atrial flutter (“AFL”), premature ventricular complexes (“PVCs”),premature atrial complexes (“PACs”), atrioventricular nodal reentranttachycardia (“AVNRT”), atrioventricular reentrant tachycardia (“AVRT”),permanent junctional reciprocating tachycardia (“PJRT”), and junctionaltachycardia (“JT”).

In some embodiments, a method performed by a computing system isprovided for classifying electromagnetic output collected from a targetthat is an electromagnetic source within a body. The method accesses aclassifier to generate a classification for electromagnetic output of anelectromagnetic source. The classifier is trained using training datagenerated from modeled electromagnetic output for a plurality of sourceconfigurations of an electromagnetic source. The modeled electromagneticoutput is generated using a computational model of the electromagneticsource that models the electromagnetic output of the electromagneticsource over time based on a source configuration. The method collectstarget electromagnetic output from the target. The method applies theclassifier to the target electromagnetic output to generate aclassification for the target. In some embodiments, the training data isgenerated by running, for each of the source configurations, asimulation that generates an electromagnetic mesh for each of aplurality of simulation intervals, each electromagnetic mesh having anelectromagnetic value for a plurality of locations of theelectromagnetic source. In some embodiments, the electromagnetic sourceis a heart, a source configuration represents a source location of aheart disorder, and the modeled electromagnetic output representsactivation of the heart, and the classifier is trained usingelectromagnetic data derived from an electrocardiogram representation ofthe electromagnetic output.

In some embodiments, one or more computing systems are provided forgenerating a classifier for classifying electromagnetic output of anelectromagnetic source. The one or more computing systems include one ormore computer-readable storage mediums and one or more processors forexecuting the computer-executable instructions stored in the one or morecomputer-readable storage mediums. The one or more computer-readablestorage mediums store a computational model of the electromagneticsource. The computational model models electromagnetic output of theelectromagnetic source over time based on a source configuration of theelectromagnetic source. The one or more computer-readable storagemediums store computer-executable instructions for controlling the oneor more computing systems to, for each of a plurality of sourceconfigurations, generate training data from the electromagnetic outputof the computational model that is based on the source configuration andtrain the classifier using the training data. In some embodiments, thecomputer-executable instructions that generate the training data for asource configuration further control the one or more computing systemsto generate derived electromagnetic data from the electromagnetic outputfor the source configuration and generate a label for theelectromagnetic data based on the source configuration.

In some embodiments, a method performed by a computing system generatinga simulated anatomy of an electromagnetic source within a body isprovided. The method accesses seed anatomies of the electromagneticsource. Each seed anatomy has a seed value for each of a plurality ofanatomical parameters of the electromagnetic source. The method accessesa set of weights that includes a weight for each seed anatomy. For eachof the anatomical parameters, the method generates a simulated value forthat anatomical parameter by combining the seed values for thatanatomical parameter, factoring in the weights of the seed anatomies. Insome embodiments, the method validates the simulated anatomy based oncomparison to values for anatomical parameters found in a population. Insome embodiments, the anatomical parameters include dimensions of theelectromagnetic source and wherein a simulated value for a dimension isvalidated when a patient in the population includes value for thatdimension that is approximately the same as the simulated value. In someembodiments, the anatomical parameters of the seed anatomies arecollected by scanning actual electromagnetic sources within bodies. Insome embodiments, the electromagnetic source is a heart. In someembodiments, the method generates a simulated value for an anatomicalparameter based on a weighted average of the seed values for thatanatomical parameter. In some embodiments, the method further generatesa plurality of simulated anatomies wherein each simulated anatomy isbased on a different set of weights.

In some embodiments, a computing system for generating simulatedanatomies of a heart is provided. The computing system comprises one ormore computer-readable storage mediums storing seed anatomies of aheart. Each seed anatomy having a seed value for each of a plurality ofanatomical parameters of a heart. The one or more computer-readablestorage mediums also stores sets of weights that each includes a weightfor each seed anatomy. The one or more computer-readable storage mediumsstore computer-executable instructions for controlling the computingsystem to, for each set of weights and for each of the anatomicalparameters for that set of weights, generate a simulated value for thatanatomical parameter by combining the seed values for that anatomicalparameter, factoring in the weights of the seed anatomies. The computingsystem further comprises one or more processors for executing thecomputer-executable instructions stored in the one or morecomputer-readable storage mediums. In some embodiments, the instructionsfurther control the computing system to validate each simulated anatomybased on comparison to values for anatomical parameters found in apopulation. In some embodiments, the anatomical parameters includethickness of walls of a heart and dimensions of a chamber of the heart.In some embodiments, a simulated value for a dimension is validated whena patient in the population includes value for that dimension that isapproximately the same as the simulated value. In some embodiments, theseed anatomies represent extremes of hearts found in a population. Insome embodiments, the anatomical parameters of the seed anatomies arecollected by scanning hearts. In some embodiments, the generating of asimulated value for an anatomical parameter is based on a weightedaverage of the seed values for that anatomical parameter. In someembodiments, a method performed by a computing system for generating anarrhythmia model library for modeling a heart is provided. The methodaccesses simulated anatomies of anatomical parameters of the heart. Thesimulated anatomies are generated based on seed anatomies of anatomicalparameters of the heart and sets of weights that include a weight foreach seed anatomy. The method accesses configuration parameters thatinclude one or more of torso anatomy, normal and abnormal cardiacanatomy, normal and abnormal cardiac tissue, scar, fibrosis,inflammation, edema, accessory pathways, congenital heart disease,malignancy, sites of prior ablation, sites of prior surgery, sites ofexternal radiation therapy, pacing leads, implantablecardioverter-defibrillator leads, cardiac resynchronization therapyleads, pacemaker pulse generator location, implantablecardioverter-defibrillator pulse generator location, subcutaneousdefibrillator lead location, subcutaneous defibrillator pulse generatorlocation, leadless pacemaker location, other implanted hardware (e.g.,right or left ventricular assist devices), external defibrillationelectrodes, surface ECG leads, surface mapping leads, a mapping vest,and other normal and pathophysiologic feature distributions within theheart, action potential dynamics for the heart, sets of conductivitiesfor the heart, arrhythmia source locations within the heart, and so on.The method establishes source configurations that are each based on asimulated anatomy and a combination of electrophysiology parameters. Foreach of a plurality of the source configurations, the method generates amesh based on the simulated anatomy of that source configuration, themesh having vertices, and for each vertex of the mesh, generates modelparameters of a computational model of the heart based on thecombination of electrophysiology parameters of that sourceconfiguration. The computational model for modeling electromagneticpropagation at that vertex based on the electrophysiology parameters ofthat source configuration. In some embodiments, the method generates asimulated anatomy by accessing seed anatomies of a heart where each seedanatomy has a seed value for each of the anatomical parameters of theheart; accesses a set of weights that includes a weight for each seedanatomy; and for each of the anatomical parameters, generates asimulated value for that anatomical parameter by combining the seedvalues for that anatomical parameter, factoring in the weights of theseed anatomies. In some embodiments, the simulated anatomies arevalidated based on comparison to values for anatomical parameters foundin a population. In some embodiments, the anatomical parameters of theseed anatomies are collected by scanning actual hearts. In someembodiments, for each of a plurality of source configurations, themethod generates a modeled electromagnetic output of the heart for thatsource configuration using a computational model for the heart. In someembodiments, for each source configuration, the method generatestraining data for the modeled electromagnetic output that is based onthat source configuration; and trains a classifier for classifyingelectromagnetic output of the heart using the training data.

In some embodiments, a computing system for generating a model libraryof models of an electromagnetic source within a body is provided. Thecomputing system comprises one or more computer-readable storage mediumsstoring computer-executable and one or more processors for executing thecomputer-executable instructions stored in the one or morecomputer-readable storage mediums. The instructions control thecomputing system to generate simulated anatomies of anatomicalparameters of the electromagnetic source from seed anatomies andgenerate source configurations that are each based on a simulatedanatomy and a combination of configuration parameters. The instructionscontrol the computing system to, for each of a plurality of the sourceconfigurations, generate a mesh based on the simulated anatomy of thatsource configuration, the mesh having vertices; and for each vertex ofthe mesh, generate model parameters of a computational model of theelectromagnetic source based on the combination of configurationparameters of that source configuration. In some embodiments, thecomputational model for modeling electromagnetic propagation at a vertexis based on the configuration parameters of a source configuration. Insome embodiments, the simulated anatomies are generated based onanatomical parameters of the seed anatomies and sets of weights thatinclude a weight for each seed anatomy. In some embodiments, theelectromagnetic source is a heart and the configuration parametersinclude one or more of torso anatomy, normal and abnormal cardiacanatomy, normal and abnormal cardiac tissue, scar, fibrosis,inflammation, edema, accessory pathways, congenital heart disease,malignancy, sites of prior ablation, sites of prior surgery, sites ofexternal radiation therapy, pacing leads, implantablecardioverter-defibrillator leads, cardiac resynchronization therapyleads, pacemaker pulse generator location, implantablecardioverter-defibrillator pulse generator location, subcutaneousdefibrillator lead location, subcutaneous defibrillator pulse generatorlocation, leadless pacemaker location, other implanted hardware (e.g.,right or left ventricular assist devices), external defibrillationelectrodes, surface ECG leads, surface mapping leads, a mapping vest,and other normal and pathophysiologic feature distributions within theheart, action potential dynamics for the heart, sets of conductivitiesfor the heart, and arrhythmia source locations within the heart. In someembodiments, the simulated anatomies are validated based on comparisonto values for anatomical parameters found in a population. In someembodiments, the anatomical parameters of the seed anatomies arecollected by scanning actual electromagnetic sources. In someembodiments, the computer-executable instructions further control thecomputing system to, for each of a plurality of source configurations,generate a modeled electromagnetic output of the electromagnetic sourcefor that source configuration using a computational model for theelectromagnetic source. In some embodiments, computer-executableinstructions further control the computing system to for each sourceconfiguration, generate training data for the modeled electromagneticoutput that is based on that source configuration; and train aclassifier for classifying electromagnetic output of the electromagneticsource using the training data.

In some embodiments, a method performed by a computing system forgenerating a model library of models of an electromagnetic source withina body is provided. The method accesses simulated anatomies ofanatomical parameters of the electromagnetic source. The methodgenerates source configurations that are each based on a simulatedanatomy and a combination of configuration parameters. For each of aplurality of the source configurations, the method generates a modelbased on the simulated anatomy of that source configuration, thecombination of configuration parameters of that source configuration,and a computational model of the electromagnetic source. In someembodiments, the generating of a model includes generating a mesh basedon the simulated anatomy of that source configuration and, for eachvertex of the mesh, generating model parameters of a computational modelof the electromagnetic source based on the combination of configurationparameters of that source configuration. In some embodiments, thecomputational model is for modeling electromagnetic propagation at avertex based on the configuration parameters of a source configuration.In some embodiments, the electromagnetic source is a heart and themodels are arrhythmia models. In some embodiments, the method furthergenerates the simulated anatomies based on anatomical parameters of seedanatomies and sets of weights that include a weight for each seedanatomy. In some embodiments, the electromagnetic source is a heart andthe configuration parameters include one or more of a human torso,normal and abnormal cardiac anatomy, normal and abnormal cardiac tissue,scar, fibrosis, inflammation, edema, accessory pathways, congenitalheart disease, malignancy, sites of prior ablation, sites of priorsurgery, sites of external radiation therapy, pacing leads, implantablecardioverter-defibrillator leads, cardiac resynchronization therapyleads, pacemaker pulse generator location, implantablecardioverter-defibrillator pulse generator location, subcutaneousdefibrillator lead location, subcutaneous defibrillator pulse generatorlocation, leadless pacemaker location, other implanted hardware (e.g.,right or left ventricular assist devices), external defibrillationelectrodes, surface ECG leads, surface mapping leads, a mapping vest,and other normal and pathophysiologic feature distributions within theheart, action potential dynamics for the heart, sets of conductivitiesfor the heart, arrhythmia source locations within the heart, and so on.

In some embodiments, a method performed by a computing system forpresenting weights for a simulated anatomy of a body part is provided.The method accesses seed anatomies of the body part. Each seed anatomyhas a seed value for each of a plurality of anatomical parameters of thebody part. For each of a plurality of seed anatomies of the body part,the method displays a seed representation of the body part based on seedvalues of anatomical parameters of that seed anatomy. The methodaccesses a set of weights that includes a weight for each seed anatomy.The method displays a simulated representation of the body part based ona simulated value for each anatomical parameter by, for each anatomicalparameter, combining the seed values of the seed anatomies of thatanatomical parameter, factoring in the weights of the seed anatomies. Insome embodiments, the seed representations are displayed in a circulararrangement with the simulated representation displayed within thecircular arrangement. In some embodiments, the method further displays,in association with each displayed seed representation for a seedanatomy, an indication of the weight associated with that seed anatomy.In some embodiments, the method further displays a line between eachdisplayed seed representation and the displayed simulatedrepresentation, wherein the displayed indication of the weight for aseed anatomy is displayed in association with the displayed line betweenthe displayed seed representation for that seed anatomy and thedisplayed simulated representation. In some embodiments, the methodfurther provides a user interface element for specifying the weight foreach seed anatomy. In some embodiments, the method provides a userinterface element for specifying a plurality of sets of weights, witheach set including a weight for each seed anatomy. In some embodiments,the plurality of sets of weights are specified by providing a range ofweights and an increment. In some embodiments, the body part is a heart.In some embodiments, the body part is a lung. In some embodiments, thebody part is a torso surface.

In some embodiments, a computing system for presenting a simulatedanatomy of a body part is provided. The computing system comprises oneor more computer-readable storage mediums storing computer-executableinstructions and one or more processors for executing thecomputer-executable instructions stored in the one or morecomputer-readable storage mediums. The instructions control thecomputing system to for each of a plurality of seed anatomies of thebody part, display a seed representation of the body part based on seedvalues of anatomical parameters of that seed anatomy; and display asimulated representation of the body part based on a simulated anatomywith simulated values for anatomical parameters derived from a weightedcombination of the seed values of the seed anatomies for the anatomicalparameters. In some embodiments, the computer-executable instructionsfurther control the computing system to generate the simulated anatomyby, for each anatomical parameter, generating a simulated value for thatanatomical parameter by combining the seed values of the seed anatomiesof that anatomical parameter, factoring in weights of the seedanatomies. In some embodiments, the seed representations are displayedin a circular arrangement with the simulated representation displayedwithin the circular arrangement. In some embodiments, thecomputer-executable instructions further control the computing system todisplay, in association with each displayed seed representation for aseed anatomy, an indication of a weight associated with that seedanatomy. In some embodiments, the computer-executable instructionsfurther control the computing system to provide a user interface forspecifying a plurality of sets of weights, with each set including aweight for each seed anatomy. In some embodiments, the plurality of setsof weights are specified by a range of weights and an increment.

In some embodiments, a method performed by a computing system forpresenting a simulated anatomy of a heart is provided. For each of aplurality of seed anatomies of the heart, the method displays a seedrepresentation of the heart based on seed values of anatomicalparameters of that seed anatomy. The method displays a simulatedrepresentation of the heart based on a simulated anatomy derived, foreach anatomical parameter, from a simulated value for that anatomicalparameter generated from a weighted combination of the seed values ofthe seed anatomies for that anatomical parameter. In some embodiments,the seed representations are displayed in a circular arrangement withthe simulated representation displayed within the circular (e.g.,bullseye) arrangement. In some embodiments, the method further displays,in association with each displayed seed representation for a seedanatomy, an indication of a weight associated with that seed anatomy.

In some embodiments, a method performed by a computing system forconverting a first polyhedral model to a second polyhedral model isprovided. The first polyhedral model has a first polyhedral mesh with avolume containing first polyhedrons. Each vertex of the firstpolyhedrons has a model parameter. The method generates a representationof the surface of the first polyhedral model from the first polyhedrons.The method generates a second polyhedral mesh for the second polyhedralmodel by populating the volume with the surface with second polyhedronsthat are different from the first polyhedrons. For each of a pluralityof vertices of the second polyhedrons of the second polyhedral mesh, themethod interpolates a model parameter for that vertex based on theparameters of vertices of the first polyhedrons that are proximate tothat vertex. In some embodiments, the first polyhedrons are hexahedronsand the second polyhedrons are tetrahedrons. In some embodiments, thepolyhedral meshes represent a body part. In some embodiments, the bodypart is a heart. In some embodiments, the first polyhedral mesh has anorigin and further comprising, prior to interpolating the modelparameters, mapping the second polyhedral mesh to the same origin. Insome embodiments, each vertex of the first polyhedrons has multipleparameters and the interpolating interpolates each model parameter. Insome embodiments, the first polyhedral model and the second polyhedralmodel represent computational models of an electromagnetic source withina body, and the method further generates a modeled electromagneticoutput of the electromagnetic source based on the second polyhedralmodel using a problem solver adapted to operate on meshes with secondpolyhedrons. In some embodiments, the electromagnetic source is a heart,and the method further generates a vectorcardiogram from the modeledelectromagnetic output. In some embodiments, the first polyhedral modeland the second polyhedral model are geometric models (e.g., of cardiacor torso anatomy). In some embodiments, the first polyhedral model andthe second polyhedral model represent models of an electromagneticsource within a body, and the method further converts a plurality offirst polyhedral models representing different source configurations. Insome embodiments, the electromagnetic source is a heart and a sourceconfiguration specifies one or more of a fiber architecture, torsoanatomy, normal and abnormal cardiac anatomy, normal and abnormalcardiac tissue, scar, fibrosis, inflammation, edema, accessory pathways,congenital heart disease, malignancy, sites of prior ablation, sites ofprior surgery, sites of external radiation therapy, pacing leads,implantable cardioverter-defibrillator leads, cardiac resynchronizationtherapy leads, pacemaker pulse generator location, implantablecardioverter-defibrillator pulse generator location, subcutaneousdefibrillator lead location, subcutaneous defibrillator pulse generatorlocation, leadless pacemaker location, other implanted hardware (e.g.,right or left ventricular assist devices), external defibrillationelectrodes, surface ECG leads, surface mapping leads, a mapping vest,and other normal and pathophysiologic feature distributions within theheart, action potential dynamics, conductivities, arrhythmia sourcelocation or locations, and so on.

In some embodiments, a computing system for converting a firstpolyhedral model of a body part to a second polyhedral model of the bodypart is provided. The computing system comprises one or morecomputer-readable storage mediums storing computer-executableinstructions and one or more processors for executing thecomputer-executable instructions stored in the one or morecomputer-readable storage mediums. The instructions for controlling thecomputing system to generate a representation of the surface of thefirst polyhedral model. The first polyhedral model has a firstpolyhedral mesh based on a first polyhedron. The instructions forcontrolling the computing system to generate a second polyhedral meshfor the second polyhedral model by populating the volume with thesurface based on a second polyhedron that is different from the firstpolyhedron. The instructions for controlling the computing system to foreach of a plurality of vertices of second polyhedrons of the secondpolyhedral mesh, interpolate a model parameter for that vertex based onthe parameters of vertices of first polyhedrons of the first polyhedralmesh that are proximate to that vertex. In some embodiments, the firstpolyhedron is a hexahedron and the second polyhedron is a tetrahedron.In some embodiments, the first polyhedral mesh has an origin and whereinthe computer-executable instructions further control the computingsystem to, prior to interpolating the model parameters, map the secondpolyhedral mesh to the same origin. In some embodiments, the firstpolyhedral model and the second polyhedral model represent computationalmodels of the heart and wherein the computer-executable instructionsfurther control the computing system to generate a modeledelectromagnetic output of the electromagnetic heart based on the secondpolyhedral model using a problem solver adapted to operate on mesheswith second polyhedrons. In some embodiments, the computer-executableinstructions further control the computing system to generate avectorcardiogram from the modeled electromagnetic output. In someembodiments, the first polyhedral model and the second polyhedral modelare geometric models (e.g., of cardiac or torso anatomy).

In some embodiments, a method performed by a computing system forconverting a first polyhedral model to a second model is provided. Thefirst polyhedral model has a first polyhedral mesh with a volumecontaining first polyhedrons. Each vertex of the first polyhedrons has amodel parameter. The method generates a representation of the surface ofthe first polyhedral model from the first polyhedrons. For each of aplurality of points of a second model, the method interpolates a modelparameter for that point based on the parameters of vertices of thefirst polyhedrons that are considered proximate to that vertex. In someembodiments, the second model is a second polyhedral model and thepoints are vertices of the second polyhedral model. In some embodiments,the second model is represented by regularly spaced grid points and thepoints are grid points.

In some embodiments, a method performed by a computing device forgenerating derived electromagnetic data for an electromagnetic sourcewithin a body is provided. The method accesses modeled electromagneticoutput for a first model of the electromagnetic source over time. Thefirst model is based on a first source configuration specifying a firstanatomy. The modeled electromagnetic output is generated using acomputational model of the electromagnetic source. The computationalmodel is for generating modeled electromagnetic output of theelectromagnetic source over time based on a model based on a sourceconfiguration. The method accesses a second model of the electromagneticsource based on a second source configuration specifying a secondanatomy. The method generates derived electromagnetic data for thesecond model of the electromagnetic source based on the modeledelectromagnetic output for the first model of the electromagneticsource, factoring in differences between the first anatomy and thesecond anatomy. In some embodiments, the electromagnetic source is aheart and the derived electromagnetic data is a cardiogram. In someembodiments, the cardiogram is a vectorcardiogram. In some embodiments,the cardiogram is an electrocardiogram. In some embodiments, the modeledelectromagnetic output for a model includes, for each of a plurality oftime intervals, an electromagnetic mesh with a modeled electromagneticvalue for each of a plurality of vertices of the electromagnetic mesh.In some embodiments, the modeled electromagnetic output is a collectionof voltage solutions.

In some embodiments, a computing system for generating a cardiogram fora heart is provided. The computing system comprises one or morecomputer-readable storage mediums storing a modeled electromagneticoutput for a first arrhythmia model of the heart over time. The firstarrhythmia model is based on a first anatomy. The modeledelectromagnetic output is generated using a computational model of aheart, the computational model for generating modeled electromagneticoutput of the heart over time based on an arrhythmia model. The one ormore computer-readable storage mediums store a second arrhythmia modelbased on a second anatomy. The one or more computer-readable storagemediums store computer-executable instructions for controlling thecomputing system to generate a cardiogram for the second arrhythmiamodel based on the modeled electromagnetic output for the firstarrhythmia model, factoring in differences between the first anatomy andthe second anatomy. The computing system comprising one or moreprocessors for executing the computer-executable instructions stored inthe one or more computer-readable storage mediums. In some embodiments,the cardiogram is a vectorcardiogram. In some embodiments, thecardiogram is an electrocardiogram. In some embodiments, the modeledelectromagnetic output for an arrhythmia model includes, for each of aplurality of time intervals, an electromagnetic mesh with a modeledelectromagnetic value for each of a plurality of vertices of theelectromagnetic mesh. In some embodiments, the modeled electromagneticoutput is a collection of voltage solutions.

In some embodiments, a method performed by a computing system forbootstrapping the generating of modeled electromagnetic output of anelectromagnetic source within a body is provided. The method accessesfirst modeled electromagnetic output for a first model with a firstsource configuration of the electromagnetic source at first simulationintervals. The first modeled electromagnetic output is generated using acomputational model of the electromagnetic source. The methodinitializes second modeled electromagnetic output for a second modelwith a second source configuration of the electromagnetic source to afirst modeled electromagnetic output for one of the first simulationintervals. For each of a plurality of second simulation intervals, themethod generates using the computational model second modeledelectromagnetic output for the second model of the electromagneticsource based on the initialized second modeled electromagnetic output.In some embodiments, the electromagnetic source is a heart and thesecond source configuration is different from the first sourceconfiguration based on scar or fibrosis or pro-arrhythmic substratelocation within the heart. In some embodiments, the electromagneticsource is a heart and the first model and the second model arearrhythmia models. In some embodiments, the modeled electromagneticoutput for a model includes, for each of a plurality of time intervals,an electromagnetic mesh with a modeled electromagnetic value for each ofa plurality of vertices of the electromagnetic mesh. In someembodiments, the method further, for each of a plurality of the firstsimulation intervals, generates using the computational model the firstmodeled electromagnetic output for the first model. In some embodiments,the initializing initializes the second modeled electromagnetic outputto the first modeled electromagnetic output for a first simulationinterval after the first modeled electromagnetic output for the firstsimulation intervals has stabilized. In some embodiments, theelectromagnetic source is a heart and the first modeled electromagneticoutput has stabilized into a rhythm.

In some embodiments, a computing system for bootstrapping the generatingof modeled electromagnetic output of a heart is provided. The computingsystem comprises one or more computer-readable storage mediums storing afirst modeled electromagnetic output for a first arrhythmia model of theheart at first simulation intervals, the first modeled electromagneticoutput generated using a computational model of a heart. The one or morecomputer-readable storage mediums also store computer-executableinstructions for controlling the computing system to initialize secondmodeled electromagnetic output for a second arrhythmia model of theheart to a first modeled electromagnetic output for one of the firstsimulation intervals; and simulate using the computational model secondmodeled electromagnetic output for the second arrhythmia model based onthe initialized second modeled electromagnetic output. The computingsystem also comprises one or more processors for executing thecomputer-executable instructions stored in the one or morecomputer-readable storage mediums. In some embodiments, the firstarrhythmia model and the second arrhythmia model are based on differentscar or fibrosis or pro-arrhythmic substrate locations within the heart.In some embodiments, the modeled electromagnetic output for anarrhythmia model includes, for each of a plurality of time intervals, anelectromagnetic mesh with a modeled electromagnetic value for each of aplurality of vertices of the electromagnetic mesh. In some embodiments,the computer-executable instructions further control the computingsystem to, for each of a plurality of the first simulation intervals,generate using the computational model the first modeled electromagneticoutput for the first arrhythmia model. In some embodiments, thecomputer-executable instructions that initialize control the computingsystem to initialize the second modeled electromagnetic output to thefirst modeled electromagnetic output for a first simulation intervalafter the first modeled electromagnetic output for the first simulationintervals has stabilized. In some embodiments, the first modeledelectromagnetic output has stabilized into a rhythm.

In some embodiments, a method performed by a computing system foridentifying derived electromagnetic data that matches patientelectromagnetic data collected from a patient is provided. Theelectromagnetic data represents electromagnetic output anelectromagnetic source within a body. The method, for each of aplurality of model source configurations of the electromagnetic source,accesses a mapping of that model source configuration to derivedelectromagnetic data that is derived based on that model sourceconfiguration. The method accesses a patient source configurationrepresenting a source configuration for the electromagnetic sourcewithin the patient. The method identifies model source configurationsthat match the patient source configuration. The method identifies, fromthe derived electromagnetic data to which the identified model sourceconfigurations are mapped, derived electromagnetic data that matches thepatient electromagnetic data. In some embodiments, the derivedelectromagnetic data is derived from modeled electromagnetic outputgenerated based on a model source configuration using a computationalmodel of the electromagnetic source. In some embodiments, the modeledelectromagnetic data for a model source configuration includes, for eachof a plurality of time intervals, an electromagnetic mesh with a modeledelectromagnetic value for each of a plurality of locations of theelectromagnetic source. In some embodiments, the method further, when anidentified model source configuration has a value an anatomicalparameter that does not match the value of that anatomical parameter ofthe patient source configuration, generates adjusted derivedelectromagnetic data based on the modeled electromagnetic output forthat model source configuration and difference in the value. In someembodiments, a source configuration includes configuration parametersincluding anatomical parameters and electrophysiology parameters. Insome embodiments, the electromagnetic source is a heart and theconfiguration parameters include a scar or fibrosis or pro-arrhythmicsubstrate location within the heart, an action potential for the heart,a conductivity for the heart, and an arrhythmia location. In someembodiments, the electromagnetic source is a heart and the anatomicalparameters include dimensions of chambers of the heart, wall thicknessesof the heart, and orientation of the heart. In some embodiments, theelectromagnetic source is a heart and derived electromagnetic data is acardiogram. In some embodiments, a model source configuration includes adisorder parameter relating to an attribute of the electromagneticsource such that derived electromagnetic data for the model sourceconfiguration is based on that attribute. In some embodiments, theelectromagnetic source is a heart and the attribute is based on anarrhythmia. In some embodiments, the identifying of derivedelectromagnetic data that matches the patient electromagnetic data isbased on a Pearson correlation coefficient, a root-mean-squared error,and so on. In some embodiments, the identifying of derivedelectromagnetic data that matches the patient electromagnetic data isbased on root-mean-squared error. In some embodiments, a sourceconfiguration includes configuration parameters, the model sourceconfigurations include a value for each configuration parameter, and thepatient source configuration includes a value for only a proper subsetof the configuration parameters. In some embodiments, the derivedelectromagnetic data is based on a model orientation of theelectromagnetic source and further comprising when a patient orientationof the electromagnetic source of the patient is different from the modelorientation, the identifying of the derived electromagnetic data factorsin the difference between the model orientation and the patientorientation. In some embodiments, the electromagnetic source is a heartand the electromagnetic data is a vectorcardiogram and wherein theidentifying of the derived electromagnetic data includes generating arotation matrix based on the difference between the model orientationand the patient orientation and rotating a vectorcardiogram based on therotation matrix.

In some embodiments, a method performed by a computing system forgenerating a classification for a patient based on patientelectromagnetic data representing electromagnetic output of anelectromagnetic source within the patient is provided. The method, foreach of a plurality of clusters (e.g., groups) of model sourceconfigurations of the electromagnetic source, accesses a classifier forthat cluster that is trained based on the model source configurations ofthat cluster to generate a classification for derived electromagneticdata. The method accesses a patient source configuration representing asource configuration for the electromagnetic source within the patient.The method identifies a cluster whose model source configurationsmatches the patient source configuration. The method applies theclassifier for the identified cluster to the patient electromagneticdata to generate a classification for the patient. In some embodiments,the derived electromagnetic data is derived from modeled electromagneticoutput generated based on a model source configuration using acomputational model of the electromagnetic source. In some embodiments,a source configuration includes configuration parameters includinganatomical parameters and electrophysiology parameters. In someembodiments, the electromagnetic source is a heart and the configurationparameters include a scar or fibrosis or pro-arrhythmic substratelocation within the heart, an action potential for the heart, and aconductivity for the heart. In some embodiments, the electromagneticsource is a heart and the anatomical parameters include dimensions ofchambers of the heart and wall thicknesses of the heart. In someembodiments, the electromagnetic source is a heart and derivedelectromagnetic data is a cardiogram. In some embodiments, thecardiogram is a vectorcardiogram. In some embodiments, a model sourceconfiguration matches the patient source configuration based on a cosinesimilarity. In some embodiments, the method further generates clustersof model source configurations; and for each of the clusters, for eachof the model configuration sources of that cluster, generates asimulated electromagnetic output of the electromagnetic source based onthat model configuration source; and generates derived electromagneticdata for that model source configuration from the simulatedelectromagnetic output based on that model source configuration. In someembodiments, a source configuration includes configuration parameters,the model source configurations include a value for each configurationparameter, and the patient source configuration includes a value foronly a proper subset of the configuration parameters. In someembodiments, the derived electromagnetic data is based on a modelorientation of the electromagnetic source, and the method further, whena patient orientation of the electromagnetic source of the patient isdifferent from the model orientation, adjusts the patientelectromagnetic data based on the difference between the modelorientation and the patient orientation. In some embodiments, theelectromagnetic source is a heart and the classification is based onsource location of an arrhythmia.

In some embodiments, a computing system for identifying a modelcardiogram that matches a patient cardiogram collected from a patient isprovided. The computing system comprises one or more computer-readablestorage mediums storing: for each of a plurality of model sourceconfigurations of a heart, a modeled cardiogram for that model sourceconfiguration; a patient source configuration representing the patient'sheart; and computer-executable instructions that, when executed control,the computing system to: identify model source configurations that matchthe patient source configuration; and identify, from the modeledcardiograms for the identified model source configurations, thosemodeled cardiograms that match the patient cardiogram. The computingsystem includes one or more processor for executing thecomputer-executable instructions stored in the one or morecomputer-readable storage mediums. In some embodiments, a modeledcardiogram is generated from modeled electromagnetic output of a heartwith a model source configuration using a computational model of theheart. In some embodiments, a cardiogram is adjusted based ondifferences in values of an anatomical parameter for a model sourceconfiguration and the patient source configuration. In some embodiments,the model source configurations include a scar or fibrosis orpro-arrhythmic substrate location within the heart, an action potentialfor the heart, a conductivity for the heart, and an arrhythmia location.In some embodiments, the model source configurations include dimensionsof chambers of the heart, wall thicknesses of the heart, and orientationof the heart. In some embodiments, a source configuration includesconfiguration parameters, the model source configurations include avalue for each configuration parameter, and the patient sourceconfiguration includes a value for only a proper subset of theconfiguration parameters. In some embodiments, modeled cardiograms arebased on a model orientation of a heart and when a patient orientationof the heart is different from the model orientation, the identifying ofmodeled cardiograms factors in the difference between the modelorientation and the patient orientation.

In some embodiments, a method performed by one or more computing systemsfor generating a patient classifier for classifying derivedelectromagnetic data derived from electromagnetic output of anelectromagnetic source within a body is provided. The method accesses amodel classifier for generating a classification for electromagneticoutput of an electromagnetic source. The model classifier has modelclassifier weights learned based on training data that includes modeledderived electromagnetic data and model classifications. The modeledderived electromagnetic data is derived from modeled electromagneticoutput generated using a computational model of the electromagneticsource that models the electromagnetic output of the electromagneticsource over time based on a source configuration. The method accessespatient training data that includes, for each of a plurality ofpatients, patient derived electromagnetic data and a patientclassification for that patient. The method initializes patientclassifier weights of the patient classifier based on the modelclassifier weights. The method trains the patient classifier with thepatient training data and with the initialized patient classifierweights. In some embodiments, the classifier is a convolutional neuralnetwork. In some embodiments, the convolutional neural network inputs aone-dimensional image. In some embodiments, the model classifier istrained using training data that includes modeled derivedelectromagnetic data derived from modeled electromagnetic outputgenerated based on source configurations that are identified as beingsimilar to source configurations of the patients. In some embodiments,the electromagnetic source is a heart, a source configuration representsanatomical parameters and electrophysiology parameters of the heart, themodeled electromagnetic output represents activation of the heart, andthe derived electromagnetic data is based on body-surface measurements.In some embodiments, a electrophysiology parameter is based on a heartdisorder that is selected from a group consisting of but not limited tosinus rhythm, inappropriate sinus tachycardia, ectopic atrial rhythm,junctional rhythm, ventricular escape rhythm, atrial fibrillation,ventricular fibrillation, focal atrial tachycardia, atrial microreentry,ventricular tachycardia, atrial flutter, premature ventricularcomplexes, premature atrial complexes, atrioventricular nodal reentranttachycardia, atrioventricular reentrant tachycardia, permanentjunctional reciprocating tachycardia, and junctional tachycardia. Insome embodiments, the electromagnetic data is a cardiogram. In someembodiments, the classification is a source location. In someembodiments, the patient derived electromagnetic data for a patient isderived from patient electromagnetic output of the electromagneticsource of that patient. In some embodiments, the method further receivestarget patient derived electromagnetic data for a target patient andapplies the patient classifier to the target patient derivedelectromagnetic data to generate a classification for the targetpatient.

In some embodiments, a method performed by one or more computing systemsfor classifying patient derived electromagnetic data for a targetpatient is provided. The patient derived electromagnetic data is derivedfrom patient electromagnetic output of an electromagnetic source withinthe patient's body. The method accesses a patient classifier to generatea classification for patient derived electromagnetic data of theelectromagnetic source. The classifier is trained using weights of amodel classifier and patient training data, the model classifier trainedusing modeled derived electromagnetic data and model classifications.The modeled derived electromagnetic data is generated from modeledelectromagnetic output. The modeled electromagnetic output is generatedfor a plurality of source configurations using a computational model ofthe electromagnetic source. The patient training data includes patientderived electromagnetic data and patient classifications. The methodreceives the patient derived electromagnetic data for the target patientand applies the patient classifier to the received patient derivedelectromagnetic data to generate a patient classification for the targetpatient. In some embodiments, the electromagnetic source is a heart, asource configuration represents anatomical parameters andelectrophysiology parameters of the heart, the modeled electromagneticoutput represents activation of the heart, and the derivedelectromagnetic data is based on body-surface measurements.

In some embodiments, a computing system for generating a patientclassifier for classifying a cardiogram is provided. The computingsystem comprises one or more computer-readable storage mediums storingcomputer-executable instructions and one or more processors forexecuting the computer-executable instructions stored in the one or morecomputer-readable storage mediums. The instructions control thecomputing system to initialize patient classifier weights of the patientclassifier to model classifier weights of a model classifier. The modelclassifier is trained based on modeled cardiograms generated based on acomputational model of the heart applied to model heart configurations.The instructions control the computing system to train the patientclassifier with the patient training data and with the initializedpatient classifier weights, the patient training data including, foreach of a plurality of patients, a patient cardiogram and a patientclassification for that patient. In some embodiments, the modelclassifier and the patient classifier are convolutional neural networks.In some embodiments, the convolutional neural networks input aone-dimensional image. In some embodiments, the model classifier and thepatient classifier are neural networks. In some embodiments, the modelclassifier is trained using modeled cardiograms generated based on modelheart configurations that are similar to patient heart configurations ofthe patients. In some embodiments, the computer-executable instructionsfurther control the computing system to, for each of a plurality ofclusters of similar patients, train a cluster patient classifier basedon patient training data that includes cardiograms and patientclassifications for the patients in that cluster. In some embodiments,the computer-executable instructions further control the computingsystem to identify similar patients based on comparison of patient heartconfigurations of the patients. In some embodiments, thecomputer-executable instructions further control the computing system toidentify similar patients based on comparison of cardiograms of thepatients. In some embodiments, the computer-executable instructionsfurther control the computing system to identify a cluster of similarpatients that are similar to a target patient and apply the clusterpatient classifier for that identified cluster to a target patientcardiogram of the target patient to generate a target patientclassification for the target patient.

In some embodiments, a method performed by a computing system forgenerating a classification for a target patient based on a targetcardiogram of the target patient is provided. The method generates apatient classifier based on patient training data that includescardiograms of patients and based on a transference from a modelclassifier generated based on model training data that includes modeledcardiograms. The modeled cardiograms are generated based on acomputational model of the heart and model heart configurations. Themethod applies the patient classifier to the target cardiogram togenerate a target classification for the target patient.

In some embodiments, a method performed by one or more computing systemsfor generating a patient-specific model classifier for classifyingderived electromagnetic data derived from electromagnetic output of anelectromagnetic source within a body is provided. The method identifiesmodels that are similar to a target patient. For each model that isidentified, the method applies a computational model of theelectromagnetic source to generate modeled electromagnetic output of theelectromagnetic source based on model source configuration for thatmodel; derives modeled derived electromagnetic data from the generatedmodeled electromagnetic output for that model; and generates a label forthat model. The method trains the patient-specific model classifier withthe modeled derived electromagnetic data and the generated labels astraining data. In some embodiments, the classifier is a convolutionalneural network that inputs a one-dimensional image. In some embodiments,the similarity between a model and the target patient is based on sourceconfigurations. In some embodiments, the similarity between a model andthe target patient is based on derived electromagnetic data. In someembodiments, the electromagnetic source is a heart, a sourceconfiguration represents anatomical parameters and electrophysiologyparameters of the heart, the modeled electromagnetic output representsactivation of the heart, and the derived electromagnetic data is basedon body-surface measurements. In some embodiments, the electromagneticdata is a cardiogram. In some embodiments, the label represents a sourcelocation for a disorder of the electromagnetic source. In someembodiments, the training is based on a transference from a modelclassifier generated based on model training data that includes modeledderived electromagnetic data, the modeled derived electromagnetic datagenerated based on a computational model of the electromagnetic sourceand model source configurations. In some embodiments, the methodfurther, for each of a plurality of clusters of similar target patients,trains a cluster-specific model classifier based on derivedelectromagnetic data for models that are similar to the target patientsof that cluster. In some embodiments, the method further identifies acluster whose target patients are similar to another target patient andapplies the cluster-specific model classifier for that identifiedcluster to the target patient derived electromagnetic data of the othertarget patient to generate a target patient label for the other targetpatient. In some embodiments, the method further identifies the clusterof target patients based on comparison of patient source configurationsof target patients. In some embodiments, the method further identifiesthe cluster of target patients based on comparison of patient derivedelectromagnetic data of the target patients.

In some embodiments, a computing system for generating apatient-specific model classifier for classifying a cardiogram of atarget patient is provided. The computing system comprises one or morecomputer-readable storage mediums storing computer-executableinstructions and one or more processors for executing thecomputer-executable instructions stored in the one or morecomputer-readable storage mediums. The instructions control thecomputing system to identify models that are similar to the targetpatient; and train the patient-specific model classifier based ontraining data that includes modeled cardiograms and modelclassifications of the identified models. The modeled cardiograms aregenerated using a computational model of the heart based on model heartconfigurations of the identified models. In some embodiments, the modelclassifications represent a source location for a heart disorder. Insome embodiments, the training of the patient-specific model classifieris based on a transference from a model classifier generated based onmodel training data that includes modeled cardiograms. In someembodiments, the computer-executable instructions further control thecomputer system to, for each of a plurality of clusters of similartarget patients, train a cluster-specific model classifier based ontraining data that includes modeled cardiograms and model classificationof models that are similar to the target patients of that cluster. Insome embodiments, the computer-executable instructions further controlthe computer system to identify a cluster whose target patients who aresimilar to another target patient and apply the cluster-specific modelclassifier for that identified cluster to a target patient cardiogram ofthe other target patient to generate a target patient classification forthe other target patient. In some embodiments, the computer-executableinstructions control the computer system to identify the clusters ofsimilar target patients based on comparison of patient heartconfigurations of target patients. In some embodiments, thecomputer-executable instructions control the computer system to identifythe clusters of similar target patients based on comparison of patientcardiograms of the target patients.

In some embodiments, a method performed by a computing system forgenerating a representation of an electromagnetic source is provided.The method identifies modeled derived electromagnetic data that matchespatient derived electromagnetic data of a patient. The modeled derivedelectromagnetic data is derived from modeled electromagnetic output ofthe electromagnetic source generated using a computational model of theelectromagnetic source. The modeled electromagnetic output has, for eachof a plurality of time intervals, an electromagnetic value for locationsof the electromagnetic source. The method identifies cycle within themodeled electromagnetic output from which the matching modeled derivedelectromagnetic data was derived. For each of a plurality of displaylocations of the electromagnetic source, the method generates a displayelectromagnetic value for that display location based on theelectromagnetic values of the modeled electromagnetic output of theidentified cycle. The method generates a display representation of theelectromagnetic source that includes, for each of the plurality ofdisplay locations, a visual representation of the displayelectromagnetic value for that display location. In some embodiments,the display representation has geometry based on anatomical parametersof the electromagnetic source of the patient. In some embodiments, thevisual representation of a display electromagnetic value is a shadingbased on magnitude of the display electromagnetic value. In someembodiments, the visual representation of a display electromagneticvalue is a color selected based on magnitude of the displayelectromagnetic value. In some embodiments, the visual representation ofa display electromagnetic value is an intensity of a color based onmagnitude of the display electromagnetic value. In some embodiments, thedisplay electromagnetic value is based on a difference between theelectromagnetic value at the start of the cycle and the electromagneticvalue at the end of the cycle. In some embodiments, the method further,for each of a plurality of display intervals of the identified cycle,generates and outputs a display representation for that displayinterval. In some embodiments, the display representations are output insequence to illustrate activations of the electromagnetic source overtime. In some embodiments, when multiple instances of derivedelectromagnetic data match the patient derived electromagnetic data, thegenerating of the display electromagnetic value for a display locationis based on a combination of the electromagnetic values of the modeledelectromagnetic output from which the matching instances of the modeledderived electromagnetic data were derived. In some embodiments, thecombination is an average that is weighted based on closeness of thematch. In some embodiments, the electromagnetic source is a heart.

In some embodiments, a method performed by a computing system forgenerating a representation of a heart is provided. The methodidentifies a modeled cardiogram that is similar to a patient cardiogramof a patient. For each of a plurality of display locations of the heart,the method generates a display electromagnetic value for that displaylocation based on an electromagnetic value of modeled electromagneticoutput of a heart from which the modeled cardiogram is derived. Themodeled electromagnetic output is generated using a computational modelof the heart. The method generates a display representation of the heartthat includes, for each of the plurality of display locations, a visualrepresentation of the display electromagnetic value for that displaylocation. In some embodiments, the display representation has geometrybased on anatomical parameters of the heart of the patient. In someembodiments, the visual representation of a display electromagneticvalue is an intensity of a color based on magnitude of the displayelectromagnetic value. In some embodiments, the displayedelectromagnetic value is based on a difference between theelectromagnetic value at the start of a model cycle within the modeledelectromagnetic output and the electromagnetic value at the end of themodel cycle. In some embodiments, the model cycle is selected based onsimilarity to a patient cycle within the cardiogram. In someembodiments, the method further, for each of a plurality of displayintervals of the modeled electromagnetic output, generates and outputs adisplay representation for that display interval. In some embodiments,the display representations are output in sequence to illustrateactivations of the electromagnetic source over time.

In some embodiments, a computing system for displaying a representationof electrical activation of a heart of a patient is provided. Thecomputing system comprises one or more computer-readable storage mediumsstoring computer-executable instructions and one or more processors forexecuting the computer-executable instructions stored in the one or morecomputer-readable storage mediums. The instructions control thecomputing system to identify a modeled cardiogram that is similar to apatient cardiogram of the patient. The instructions control thecomputing system to generate a display representation of the heart thatincludes, for each of a plurality of display locations of the heart, avisual representation of a display value for that display location. Thedisplay values are based on modeled electromagnetic output of a heartfrom which the modeled cardiogram is derived. The modeledelectromagnetic output is generated using a computational model of theheart. The instructions control the computing system to display thedisplay representation. In some embodiments, the display representationhas geometry based on anatomical parameters of the heart of the patient.

In some embodiments, a method performed by a computing system forgenerating a surface representation of an electromagnetic forcegenerated by an electromagnetic source within a body is provided. Themethod accesses a sequence of vectors representing magnitude anddirection of the electromagnetic force over time. The vectors arerelative to an origin. For each pair of adjacent vectors, the methodidentifies a region based on the origin and the pair of vectors;generates a region representation of the region; and displays thegenerated region representation of the region. In some embodiments, themethod displays a representation of the electromagnetic source so thatthe displayed region representations visually emanate from theelectromagnetic source. In some embodiments, the origin is within theelectromagnetic source. In some embodiments, the electromagnetic sourceis a heart and the sequence of vectors is a vectorcardiogram. In someembodiments, the electromagnetic force has cycles and the regionrepresentations for a cycle form the surface representation for thatcycle, and the method further simultaneously displays surfacerepresentations for multiple cycles. In some embodiments, the generatedregion representations of the regions are displayed in sequence toillustrate changes in the electromagnetic force over time.

In some embodiments, a computing system for displaying a representationof vectorcardiogram is provided. The computing system comprises one ormore computer-readable storage mediums storing computer-executableinstructions and one or more processors for executing thecomputer-executable instructions stored in the one or morecomputer-readable storage mediums. The instructions for controlling thecomputing system to generate a surface representation of a portion ofthe vectorcardiogram where the surface representation is bounded bypoints representing x, y, and z values of vectors of the portion of thevectorcardiogram; and display the generated surface representation. Insome embodiments, the computer-executable instructions further controlthe computing system to display a representation of the heart from whichthe vectorcardiogram is derived so that the displayed surfacerepresentation visually emanates from the displayed representation ofthe heart. In some embodiments, the vectors are relative to an originthat is within the heart. In some embodiments, the vectorcardiogram hascycles and wherein the computer-executable instructions further controlthe computing system to simultaneously display the surfacerepresentations for multiple cycles. In some embodiments, the generatedsurface representation is incrementally displayed to illustrate changesin the vectorcardiogram over time. In some embodiments, the surfacerepresentation is displayed on a region-by-region basis.

Although the subject matter has been described in language specific tostructural features and/or acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as example forms of implementingthe claims. In some embodiments, the MLMO system can be employed toclassify electromagnetic output of an electromagnetic source based ondifferent types of classifications. For example, the classifications mayinclude location of a heart disorder (e.g., rotor location), scar orfibrosis or pro-arrhythmic substrate location, heart geometry (e.g.,ventricle orientation), and so on. To generate the training data, theMLMO system labels the training data with the classification type thatthe classifier is to generate. Accordingly, the invention is not limitedexcept as by the appended claims.

1. A method performed by one or more computing systems for generating apatient classifier for classifying derived electromagnetic data derivedfrom electromagnetic output of an electromagnetic source within a body,the method comprising: accessing a model classifier for generating aclassification for electromagnetic output of an electromagnetic source,the model classifier having model classifier weights learned based ontraining data that includes modeled derived electromagnetic data andmodel classifications, the modeled derived electromagnetic data derivedfrom modeled electromagnetic output generated using a computationalmodel of the electromagnetic source that models the electromagneticoutput of the electromagnetic source over time based on a sourceconfiguration; accessing patient training data that includes, for eachof a plurality of patients, patient derived electromagnetic data and apatient classification for that patient; initializing patient classifierweights of the patient classifier based on the model classifier weights;and training the patient classifier with the patient training data andwith the initialized patient classifier weights.
 2. The method of claim1 wherein the classifier is a convolutional neural network.
 3. Themethod of claim 2 wherein the convolutional neural network inputs aone-dimensional image.
 4. The method of claim 1 wherein the modelclassifier is trained using training data that includes modeled derivedelectromagnetic data derived from modeled electromagnetic outputgenerated based on source configurations that are identified as beingsimilar to source configurations of the patients.
 5. The method of claim1 wherein the electromagnetic source is a heart, a source configurationrepresents anatomical parameters and electrophysiology parameters of theheart, the modeled electromagnetic output represents activation of theheart, and the derived electromagnetic data is based on body-surfacemeasurements.
 6. The method of claim 5 wherein a electrophysiologyparameter is based on a heart disorder that is selected from the groupconsisting of a cardiac rhythm or arrhythmia inclusive but not limitedto sinus rhythm, inappropriate sinus tachycardia, ectopic atrial rhythm,junctional rhythm, ventricular escape rhythm, atrial fibrillation,ventricular fibrillation, focal atrial tachycardia, atrial microreentry,ventricular tachycardia, atrial flutter, premature ventricularcomplexes, premature atrial complexes, atrioventricular nodal reentranttachycardia, atrioventricular reentrant tachycardia, permanentjunctional reciprocating tachycardia, and junctional tachycardia.
 7. Themethod of claim 5 wherein the electromagnetic data is a cardiogram. 8.The method of claim 5 wherein the classification is a source location.9. The method of claim 1 wherein the patient derived electromagneticdata for a patient is derived from patient electromagnetic output of theelectromagnetic source of that patient.
 10. The method of claim 1further comprising: receiving target patient derived electromagneticdata for a target patient; and applying the patient classifier to thetarget patient derived electromagnetic data to generate a classificationfor the target patient.
 11. A method performed by one or more computingsystems for classifying patient derived electromagnetic data for atarget patient, the patient derived electromagnetic data derived frompatient electromagnetic output of an electromagnetic source within thepatient's body, the method comprising: accessing a patient classifier togenerate a classification for patient derived electromagnetic data ofthe electromagnetic source, the classifier trained using weights of amodel classifier and patient training data, the model classifier trainedusing modeled derived electromagnetic data and model classifications,the modeled derived electromagnetic data generated from modeledelectromagnetic output, the modeled electromagnetic output generated fora plurality of source configurations using a computational model of theelectromagnetic source, the patient training data including patientderived electromagnetic data and patient classifications; receiving thepatient derived electromagnetic data for the target patient; andapplying the patient classifier to the received patient derivedelectromagnetic data to generate a patient classification for the targetpatient.
 12. The method of claim 11 wherein the electromagnetic sourceis a heart, a source configuration represents anatomical parameters andelectrophysiology parameters of the heart, the modeled electromagneticoutput represents activation of the heart, and the derivedelectromagnetic data is based on body-surface measurements.
 13. Acomputing system for generating a patient classifier for classifying acardiogram, the computing system comprising: one or morecomputer-readable storage mediums storing computer-executableinstructions for controlling the computing system to: initialize patientclassifier weights of the patient classifier to model classifier weightsof a model classifier, the model classifier being trained based onmodeled cardiograms generated based on a computational model of theheart applied to model heart configurations; and train the patientclassifier with the patient training data and with the initializedpatient classifier weights, the patient training data including, foreach of a plurality of patients, a patient cardiogram and a patientclassification for that patient; and one or more processors forexecuting the computer-executable instructions stored in the one or morecomputer-readable storage mediums.
 14. The computing system of claim 13wherein the model classifier and the patient classifier areconvolutional neural networks.
 15. The computing system of claim 14wherein the convolutional neural networks input a one-dimensional image.16. The computing system of claim 13 wherein the model classifier andthe patient classifier are neural networks.
 17. The computing system ofclaim 13 wherein the model classifier is trained using modeledcardiograms generated based on model heart configurations that aresimilar to patient heart configurations of the patients.
 18. Thecomputing system claim 13 wherein the computer-executable instructionsfurther control the computing system to, for each of a plurality ofclusters of similar patients, train a cluster patient classifier basedon patient training data that includes cardiograms and patientclassifications for the patients in that cluster.
 19. The computingsystem of claim 18 wherein the computer-executable instructions furthercontrol the computing system to identify similar patients based oncomparison of patient heart configurations of the patients.
 20. Thecomputing system of claim 18 wherein the computer-executableinstructions further control the computing system to identify similarpatients based on comparison of cardiograms of the patients.
 21. Thecomputing system of claim 18 wherein the computer-executableinstructions further control the computing system to: identify a clusterof similar patients that are similar to a target patient; and apply thecluster patient classifier for that identified cluster to a targetpatient cardiogram of the target patient to generate a target patientclassification for the target patient.
 22. A method performed by acomputing system for generating a classification for a target patientbased on a target cardiogram of the target patient, the methodcomprising: generating a patient classifier based on patient trainingdata that includes cardiograms of patients and based on a transferencefrom a model classifier generated based on model training data thatincludes modeled cardiograms, the modeled cardiograms generated based ona computational model of the heart and model heart configurations; andapplying the patient classifier to the target cardiogram to generate atarget classification for the target patient.
 23. A method performed byone or more computing systems for generating a patient-specific modelclassifier for classifying derived electromagnetic data derived fromelectromagnetic output of an electromagnetic source within a body, themethod comprising: identifying models that are similar to a targetpatient; for each model that is identified, applying a computationalmodel of the electromagnetic source to generate modeled electromagneticoutput of the electromagnetic source based on model source configurationfor that model; deriving modeled derived electromagnetic data from thegenerated modeled electromagnetic output for that model; and generatinga label for that model; and training the patient-specific modelclassifier with the modeled derived electromagnetic data and thegenerated labels as training data.
 24. The method of claim 23 whereinthe classifier is a convolutional neural network that inputs aone-dimensional image.
 25. The method of claim 23 wherein similaritybetween a model and the target patient is based on sourceconfigurations.
 26. The method of claim 23 wherein similarity between amodel and the target patient is based on derived electromagnetic data.27. The method of claim 23 wherein the electromagnetic source is aheart, a source configuration represents anatomical parameters andelectrophysiology parameters of the heart, the modeled electromagneticoutput represents activation of the heart, and the derivedelectromagnetic data is based on body-surface measurements.
 28. Themethod of claim 27 wherein the electromagnetic data is a cardiogram. 29.The method of claim 23 wherein the label represents a source locationfor a disorder of the electromagnetic source.
 30. The method of claim 23wherein the training is based on a transference from a model classifiergenerated based on model training data that includes modeled derivedelectromagnetic data, the modeled derived electromagnetic data generatedbased on a computational model of the electromagnetic source and modelsource configurations.
 31. The method of claim 23 further comprising,for each of a plurality of clusters of similar target patients, traininga cluster-specific model classifier based on derived electromagneticdata for models that are similar to the target patients of that cluster.32. The method of claim 31 further comprising: identifying a clusterwhose target patients are similar to another target patient; andapplying the cluster-specific model classifier for that identifiedcluster to the target patient derived electromagnetic data of the othertarget patient to generate a target patient label for the other targetpatient.
 33. The method of claim 32 further comprising identifying thecluster of target patients based on comparison of patient sourceconfigurations of target patients.
 34. The method of claim 32 furthercomprising identifying the cluster of target patients based oncomparison of patient derived electromagnetic data of the targetpatients.
 35. A computing system for generating a patient-specific modelclassifier for classifying a cardiogram of a target patient, thecomputing system comprising: one or more computer-readable storagemediums storing computer-executable instructions for controlling thecomputing system to: identify models that are similar to the targetpatient; and train the patient-specific model classifier based ontraining data that includes modeled cardiograms and modelclassifications of the identified models, the modeled cardiogramsgenerated using a computational model of the heart based on model heartconfigurations of the identified models; and one or more processors forexecuting the computer-executable instructions stored in the one or morecomputer-readable storage mediums.
 36. The computing system of claim 35wherein the model classifications represent a source location for aheart disorder.
 37. The computing system of claim 35 wherein thetraining of the patient-specific model classifier is based on atransference from a model classifier generated based on model trainingdata that includes modeled cardiograms.
 38. The computing system ofclaim 35 wherein the computer-executable instructions further controlthe computer system to, for each of a plurality of clusters of similartarget patients, train a cluster-specific model classifier based ontraining data that includes modeled cardiograms and model classificationof models that are similar to the target patients of that cluster. 39.The computing system of claim 38 wherein the computer-executableinstructions further control the computer system to: identify a clusterwhose target patients who are similar to another target patient; andapply the cluster-specific model classifier for that identified clusterto a target patient cardiogram of the other target patient to generate atarget patient classification for the other target patient.
 40. Thecomputing system of claim 38 wherein the computer-executableinstructions control the computer system to identify the clusters ofsimilar target patients based on comparison of patient heartconfigurations of target patients.
 41. The computing system of claim 38wherein the computer-executable instructions control the computer systemto identify the clusters of similar target patients based on comparisonof patient cardiograms of the target patients.